control strategies for hybrid renewable energy systems

CONTROL STRATEGIES FOR HYBRID RENEWABLE ENERGY SYSTEMS

CONTROL STRATEGIES FOR HYBRID RENEWABLE ENERGY SYSTEMS By

Dr. K. Sujatha, N.P.G Bhavani Dr. A.Kannan & Dr. V. Balaji Department of Electrical and Electronics Engineering Dr. M.G.R. Educational and Research Institute University (Decl.u/s.3 of UGC Act 1956) E.V.R. Periyar High Road, Maduravoyal Chennai- 600 095, India.

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TABLE OF CONTENTS

Chapter No.

Title

Page No.

1.

Introduction

1

2.

Hybrid Power System

7

3.

Controller

51

4.

Matlab Simultion of Sepic Converter Using Various Controllers

85

5.

Hardware Model

105

6.

Conclusion

139

References

141

CHAPTER - 1

INTRODUCTION

1.1

GENERAL

Power transmission is one of the challenging concerns of modern power system. Considering India, it has various power generations at different geographical locations. Each area has its own generation frequency, to match frequencies and to eliminate transmission losses we go for HVDC transmission. Environmentally friendly solutions are becoming more prominent than ever as a result of concern regarding the state of our deteriorating planet. So by harnessing renewable energy it is possible to reduce our dependence on fossil fuels. Renewable energy includes solar energy, wind energy, geo thermal energy etc. These energies when harnessed separately don’t provide reliability. Therefore we go for Hybrid energy systems. Renewable energy technologies are suitable for off-grid services, serving the remote areas without having to build or extend expensive and complicated grid infrastructure. Therefore standalone system using renewable energy sources have become a preferred option. This paper is a review of hybrid renewable energy power generation systems focusing on energy sustainability. It highlights the research on the methodology, unit sizing, optimization, storage, energy management of renewable energy system. The term hybrid power system is used to describe any power system combine two or more energy conversion devices, or two or more fuels for the same device, that when integrated, overcome limitations inherent in either. The design and structure of a hybrid energy system obviously take into account the types of renewable energy sources available locally, and the consumption the system supports. Hybrid renewable energy systems have proven to be an excellent solution for providing electricity in future. The renewable technologies have come a long way in terms of research and development. However there are still certain obstacles in terms of their efficiency

2

Control Strategies For Hybrid Renewable Energy Systems

and optimal use. Following are the challenges faced by the designer. 

The renewable energy sources, such as solar PV and FCs, need innovative technology to harness more amount of useful power from them. The poor efficiency of solar is major obstruction in encouraging its use.



The manufacturing cost of renewable energy sources needs a significant reduction because the high capital cost leads to an increased payback time.



It should be ensured that there should be minimal amount of power loss in the power electronic devices.



The storage technologies need to increase their life-cycle through inventive technologies.



These stand alone systems are less adaptable to load fluctuations. Large variation in load might even lead to entire system collapse.

This paper presents a new system configuration of the front-end rectifier stage for a hybrid wind/photovoltaic energy system. This configuration allows the two sources to supply the load separately or simultaneously depending on the availability of the energy sources. The inherent nature of this SEPIC fused boost converter, additional input filters are not necessary to filter out high frequency harmonics. Harmonic content is detrimental for the generator lifespan, heating issues, and efficiency. The fused multi input rectifier stage also allows Maximum Power Point Tracking (MPPT) to be used to extract maximum power from the wind and sun when it is available. An adaptive MPPT algorithm will be used for the wind system and a standard perturb and observe method will be used for the PV system. Operational analysis of the proposed system will be discussed in this project. Hardware implementation is done to show its efficient performance. When a source is insufficient in meeting the load demands, the other energy source can compensate for the difference. Several hybrid wind/PV power systems with MPPT control have been proposed, usage of rectifiers and inverters is also discussed.

1.2

LITERATURE SURVEY

Ahmad El Khateb, Member, Nasrudin Abd Rahim, Jeyraj Selvaraj, and M. Nasir Uddin, “Fuzzy Logic Controller Based SEPIC Converter for


3

Maximum Power Point Tracking” IEEE transactions on industry applications, vol. pp, no. 99, february 2014 The FLC proposed in this paper presents that the convergent distribution of the membership function offers faster response than the symmetrically distributed membership functions. The fuzzy controller for the SEPIC MPPT scheme shows a high precision in current transition and keeps the voltage without any changes, in variable-load case, represented in small steady state error and small overshoot. The proposed scheme ensures optimal use of photovoltaic (PV) array and proves its efficacy in variable load conditions, unity and lagging power factor at the inverter output (load) side. Hohm D. Pand Ropp M. E (2003) “Comparative Study of Maximum Power Point Tracking Algorithms”, Prog. Photovolt: Res. Appl. In this paper studies and comparison of different maximum power point tracking algorithms available and found their maximum power tracking efficiencies. Yi Zhao, Xin Xiang, Chushan Li, Yunjie Gu, Wuhua Li, and Xiangning He, “Single-Phase High Step-up Converter With Improved Multiplier Cell Suitable for Half-Bridge-Based PV Inverter System” June 2014 In this paper, a single-phase high step-up converter is proposed, designed not only to boost the relatively low photovoltaic (PV) voltage to a high bus voltage with high efficiency. The voltage gain of the converter is extended and the narrow turn-off period is avoided by using the coupled inductor multiplier. Ali Ajami, Hossein Ardi, and Amir Farakhor “A Novel High Step-up DC/DC Converter Based on Integrating Coupled Inductor and SwitchedCapacitor Techniques for Renewable Energy Applications” August 2015 The suggested high step-up dc/dc converter structure renewable energy applications consists of a coupled inductor and two voltage multiplier cells, in order to obtain high step-up voltage gain. In addition, two capacitors are charged during the switch-off period, using the energy stored in the coupled inductor which increases the voltage transfer gain. Sandeep kumar, Vijay garg, “Hybrid system of pv-solar / wind & fuel cell” International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 8, August 2013.

4


This paper gives a detailed theoretical and experimental comparison of different hybrid renewable energy resources connected to grid with complex electrical interactions. Yuvaraj V, Roger Rozario, and Deepa. S.N, “Implementation and control of Multi – Input Power Converter for Grid Connected Hybrid Renewable Energy Generation System” Student pulse June 2011, Vol.3, Issue 6. This paper summarizes a novel multi-input power converter for the grid connected hybrid renewable energy system. Through this power from the PV array or/and the wind turbine can be delivered to the utility grid individually or simultaneously, MPPT feature is realized for both PV and wind energy, and a large range of input voltage variation caused by different isolation and wind speed is made acceptable. Pablo García, Carlos Andrés García, Luis M. Fernández, Francisco Llorens, and Francisco Jurado, Senior Member, “ANFIS-Based Control of a GridConnected Hybrid System Integrating Renewable Energies, Hydrogen and Batteries” IEEE transactions on Industrial Informatics, Vol.10, No.2, May 2014. This paper describes and evaluates an adaptive neuro – fuzzy inference system (ANFIS)-based energy management system (EMS) of a grid-connected hybrid system. It presents a wind turbine (WT) and photovoltaic (PV) solar panels as primary energy sources, and an energy storage system (ESS) based on hydrogen (fuel cell–FC-, hydrogen tank and electrolyzer) and battery. An ANFIS-based supervisory control system determines the power that must be generated by/stored in the hydrogen and battery, taking into account the power demanded by the grid, the available power, the hydrogen tank level and the state-of-charge (SOC) of the battery John Darvill, Alin Tisan, Marcian Cirstea Anglia Ruskin University, Cambridge, UK “An ANFIS-PI Based Boost Converter Control Scheme” This paper presents a novel method of improving the performance of the PI controller using an ANFIS network to provide gain scheduling. This control scheme is applied to a Boost Converter circuit and simulated within the PSIM modelling environment. The simulation results indicate that using the ANFIS controller provides a fast system response with minimal errors even under dynamic operating conditions. The ANFIS controller is also shown to simplify


5

the design flow in comparison to the popular Fuzzy-PI gain scheduling method. A. Iqbali, H. Abu-Rub, Sk. M. Ahmed “Adaptive Neuro-Fuzzy Inference System based Maximum Power Point Tracking of a Solar PV Module” IEEE international energy conference 2010. This paper presents and analyses the operation of an Adaptive Neuro-Fuzzy Inference System (ANFIS) based maximum power point tracker (MPPT) for solar photovoltaic (SPV) energy generation system. The ANFIS is trained to generate the maximum power corresponding to the given solar irradiance level and temperature. The response of ANFIS based control system is highly precise and offers very fast response.

1.3

SUMMARY

From the literature survey it can be summarized that Hybrid system like Solar, Wind and Fuel cell is connected to DC grid through normal Boost converter or interleaved converter. It is analyzed that the steady state response of the hybrid system is not reached in faster manner. In order to keep the steady state error within the limit and to increase the range of voltage, SEPIC boost converter is used along with the MPPT and PWM control for SEPIC converter is provided by ANFIS. In the earlier papers the analysis is done for the voltage source inverter using PID and Fuzzy controller. In this project Adaptive Neuro-Fuzzy Inference System gets inputs from MPPT controller and generates PWM signals according to irradiance. Wind energy is harnessed in parallel to solar energy and both operate as Hybrid energy system. Different adaptive MPPT techniques have been proposed which performs well for constant irradiations but fails with varying atmospheric conditions.

1.4

OBJECTIVE

The objective of the project is to design a SEPIC converter that steps up DC voltage with the help of ANFIS controller that takes parameters from MPPT controller. The objective is to design a hardware model to demonstrate ANFIS control for DC grid interactive solar and wind hybrid system and compare the results of the controller to provide an independent and flexible control of voltage of the hybrid resources equal to that of DC grid voltage.

6 1.5

Control Strategies For Hybrid Renewable Energy Systems THESIS OUTLINE

Chapter 2: This chapter gives the concepts and operation of different components of Hybrid system like Solar and Wind including MPPT controller, converter and battery system. Chapter 3: This chapter deals with the concepts and operation controllers like PID, Fuzzy and ANFIS control along with the circuit diagram and waveforms. Chapter 4: This chapter presents the simulation of the Hybrid system in MATLAB with specifications of each subsystem parameters and the results are presented. Comparative analysis of PID, Fuzzy and ANFIS control are also analysed. Here the voltage from the Hybrid system is synchronized with the DC grid voltage. Chapter 5: In this chapter hardware model of Hybrid renewable energy source fed SEPIC converter is presented and hardware construction is discussed. Chapter 6: This chapter discusses the conclusion and future work of the project work.

CHAPTER - 2

HYBRID POWER SYSTEM

2.1

INTRODUCTION

Hybrid power systems usually integrate renewable energy sources with fossil fuel, (Diesel / petrol) based generators to provide electrical power and traditional diesel system acting as back-up in case of lack of the primary source. There are generally two accepted hybrid power system configurations: 

Systems based mainly on diesel generators with renewable energy used for reducing fuel consumption.



Systems relying on the renewable energy source with diesel generators used as a backup supply for extended periods of low renewable energy period or in cases of high load demand.

The advantages of using renewable energy sources for generating power in remote areas are obvious. The cost of transported fuel is usually expensive for such locations. Further, using fossil fuel has many concerns on the issue of climate change and global warming.

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Fig 1(a): Overall diagram of Hybrid renewable System

WIND TURBINE

PWM CONT ROL

WITH RECTIFIER

SEPIC CONVERTER

SOLAR ARRAY

MPPT

ANFIS

INVER TER

PWM CONTROL

Fig 1(b): Proposed Hybrid energy system with SEPIC converter

L O A D

Control Strategies For Hybrid Renewable Energy Systems 2.2

Solar Photovoltaic system

2.2.1

General

9

A solar panel is a packaged interconnected assembly of solar cells, also known as photovoltaic cells. The solar panel can be used as a component of a larger photovoltaic system to generate and supply electricity in commercial and residential applications. Solar panels use light energy from the sun to generate electricity through the photovoltaic effect. The structural member of a module can either be the top layer or the back layer.

Fig 2: Operation of solar cell

2.2.2

PV Components and its Functions

The various components of PV system are (a) Isolation: It is the amount of sun radiations coming on the earth, the insolation is dependent on the location and orientation of the solar cell array and provide an input energy to the system that has medium and long-term variations due to local climatologically conditions. (b) Photovoltaic Arrays: The photovoltaic array is taken here to refer to the structure of panels (modules or sub arrays) that house and support the solar cells in a photovoltaic power system. (c) Power Conditioning: For applications where photovoltaic power system are required to supply a predictable and small (less than few kilowatts) load, a simple

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direct battery charge system is usually adequate. (d) Energy Storage: Almost all the small , point of use photovoltaic power system that are in use today incorporate an energy storage element to provide during periods of inclement weather and at night.

2.2.3

PV Module Operations

A solar cell is the building block of a panel. A single solar cell can be modeled with a current source, diode and two resistors as shown in Fig 3. This model is called as single diode model of solar cell.

Fig 3: Equivalent circuit of solar cell

The cell output current, I is given by the equation ...1 Open-circuit voltage Voc of the cell is …2 Short circuit current is Isc = I L Power output from the PV array can be obtained by using the equation: Ppv (t) = Ins (t) * A*Eff(pv) Where Ins (t) = isolation data at time t (kW/ m2)

…3


11

A = area of single PV panel (m2) Effpv = overall efficiency of the PV panels and dc/dc converters. The I-V and P-V characteristic of a solar cell is shown in Fig 4.The characteristics is obtained using the Equations 1 and 2. As the solar radiation falling over the panel changes the short circuit current, open circuit voltage and the maximum power varies. Hence in order to extract the maximum energy from the panel we need to force the panel to operate at maximum power point. This can be done using different MPPT methods described in the next section. Solar cells have a complex relationship between solar irradiation, temperature and total resistance that produces a non-linear output efficiency known as the "I-V curve". It is the purpose of the MPPT system to sample the output of cells and apply a resistance (load) to obtain maximum power for any given environmental conditions.

Fig 4: P-V, I-V Curve of Solar Cell

Fig 5: A 12V 20W Solar panel

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Control Strategies For Hybrid Renewable Energy Systems Table 1: Technical Parameters of Solar Cell

2.3

WIND ENERGY GENERATING SYSTEM

Wind turbines produce electricity by using the power of the wind to drive an electrical generator. Passing over the blades, wind generates lift and exerts a turning force. The rotating blades turn a shaft inside the nacelle, which goes into a gearbox. The gearbox adjusts the rotational speed to that which is appropriate for the generator, which uses magnetic fields to convert the rotational energy into electrical energy. The power output goes to a transformer, which converts the electricity from the generator at around 700V to the appropriate voltage for the power collection system, typically 33 kV.

2.3.1

Doubly Fed Induction Generator

Currently DFIG wind turbines are increasingly used in large wind farms. A typical DFIG system is shown in the below figure. The AC/DC/AC converter consists of two components: the rotor side converter Crotor Grid side converter Cgrid .These converters are voltage source converters that use forced commutation power electronic devices (IGBTS) to synthesize AC voltage from DC voltage


13

source. A capacitor connected on DC side acts as a DC voltage source. The generator slip rings are connected to the rotor side converter, which shares a DC link with the grid side converter in a so called back -to-back configuration. The wind power captured by the turbine is converted into electric power by the IG and is transferred to grid by stator and rotor windings.

Fig 6: DFIG and Wind turbine system

2.3.2

Steady State Characteristics

The steady state performance can be explained using Steinmetz per phase equivalent circuit model as shown in figure where motor convention is used. In this figure 2.6 vs and vr are the stator and rotor voltages, is and ir are the stator and rotor currents, rs and rr are the stator and rotor resistances (per phase), Xs and Xr are stator and rotor leakage reactance‘s, Xm is the magnetizing reactance and s is slip. The steady state equivalent circuit of DFIG is shown in Fig. 6

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Fig 7: Steady state equivalent circuit of DFIG

To obtain the torque equation from the equivalent circuit, we can simplify the steady state induction motor circuit by moving Xm to the stator terminal .The rotor current Ir is expressed as

...4 The electrical torque Te, from the power balance across the stator to rotor gap, can be calculated from …5 Where the power supplied or absorbed by the controllable source injecting voltage into the rotor circuit, that is the rotor active power, Pr can be calculated

…6


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Fig 8: Torque-speed characteristics of DFIG

2.4

RECTIFIER

A rectifier is an electrical device that converts alternating current (AC), which periodically reverses direction, to direct current (DC), which flows in only one direction. The process is known as rectification. Rectifiers have many uses, but are often found serving as components of DC power supplies and high-voltage direct current power transmission systems. Rectification may serve in roles other than to generate direct current for use as a source of power. As noted, detectors of radio signals serve as rectifiers. In gas heating systems flame rectification is used to detect presence of flame. Because of the alternating nature of the input AC sine wave, the process of rectification alone produces a DC current that, though unidirectional, consists of pulses of current. Many applications of rectifiers, such as power supplies for radio, television and computer equipment, require a steady constant DC current (as would be produced by a battery). In these applications the output of the rectifier is smoothed by an electronic filter to produce a steady current.

2.5

MAXIMUM POWER POINT TRACKING

Maximum Power Point Tracking (MPPT) is the automatic adjustment of electrical load to achieve the greatest possible power harvest, during moment to moment variations of light level, shading, temperature, and

16


photovoltaic module characteristics. Solar cells have a complex relationship between solar irradiation, temperature and total resistance that produces a nonlinear output efficiency known as the "I-V curve". It is the purpose of the MPPT system to sample the output of cells and apply a resistance (load) to obtain maximum power for any given environmental conditions. PV cells have a single operating point where the values of the current (I) and Voltage (V) of the cell result in a maximum power output. These values correspond to a particular load resistance, which is equal to ratio of voltage to resistance as specified by Ohm's Law. The power is given by product of voltage and current. A PV cell has an exponential relationship between current and voltage, and the maximum power point (MPP) occurs at the knee of the curve where derivative of power with respect to voltage is zero. At this point the characteristic resistance equals that of the load resistance. Maximum power point trackers utilize some type of control circuit or logic to allow converter circuit to extract maximum power available from a cell.

2.5.1

MPPT Algorithms

Different algorithms are proposed to track the maximum power that can be obtained from a solar panel .The main type includes hill climbing method like Perturb and observe method, incremental conductance method and other methods like constant reference voltage, current methods. Many improvements are added to these general algorithms nowadays which are making them adaptive and resistant to varying weather conditions .The general algorithms are described below.

2.5.1.1 Perturb and Observe Method The concept behind the “perturb and observe” method is to modify the operating voltage or current of the photovoltaic panel until you obtain maximum power from it. For example, if increasing the voltage to a cell increases the power output of a cell, the system increases the operating voltage until the power output begins to decrease. Once this happens, the voltage is decreased to get back to the maximum power output value. This process continues until the maximum power point is reached. Thus, the power output value oscillates around a maximum power value until it stabilizes. Perturb and observe is the most commonly used MPPT method due to its ease of implementation.


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Fig 9: Flowchart for a Perturb and Observe Tracking System

2.5.1.2 Incremental Conductance Method In the incremental conductance method, the controller measures incremental changes in array current and voltage to predict the effect of a voltage change. This method requires more computation in the controller, but can track changing conditions more rapidly than the P&O method. Like the P&O algorithm, it can produce oscillations in power output. This method utilizes the incremental conductance (dI/dV) of the photovoltaic array to compute the sign of the change in power with respect to voltage (dP/dV). The conditions are: dP/dV=0, MPP is reached dP/dV0, increase the voltage by a value When the incremental conductance is zero, the output voltage is the MPP voltage. The controller maintains this voltage until the irradiation changes and the process is repeated.

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Fig 10: Flow chart of incremental conductance algorithm

2.5.1.3 Constant Voltage Method In the constant voltage method, the power delivered to the load is momentarily interrupted and the open-circuit voltage with zero current is measured. The controller then resumes operation with the voltage controlled at a fixed ratio, such as 0.76, of the open-circuit voltage. The operating point of the PV array is kept near the MPP by regulating the array voltage and matching it to a fixed reference voltage Vref. The Vref value is set equal to the maximum power point voltage of the characteristic PV module or to another calculated best fixed voltage.

2.6

DC – DC Converter

A DC-to-DC converter is an electronic circuit which converts a source of direct current (DC) from one voltage level to another. It is a class of power converter.


19

DC to DC converters are important in portable electronic devices such as cellular phones and laptop computers, which are supplied with power from batteries primarily. Such electronic devices often contain several subcircuits, each with its own voltage level requirement different from that supplied by the battery or an external supply (sometimes higher or lower than the supply voltage). Additionally, the battery voltage declines as its stored energy is drained. Switched DC to DC converters offer a method to increase voltage from a partially lowered battery voltage thereby saving space instead of using multiple batteries to accomplish the same thing. Most DC to DC converters also regulate the output voltage. Some exceptions include high-efficiency LED power sources, which are a kind of DC to DC converter that regulates the current through the LEDs, and simple charge pumps which double or triple the output voltage.

Fig 11: Comparison of various Buck-Boost Converters

2.6.1

BUCK BOOST CONVERTER

The buck–boost converter is a type of DC-to-DC converter that has an output voltage magnitude that is either greater than or less than the input voltage magnitude. It is equivalent to a flyback converter using a single inductor instead of a transformer.

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Fig 12: Simple Buck Boost converter

Two different topologies are called buck–boost converter. Both of them can produce a range of output voltages, from an output voltage much larger (in absolute magnitude) than the input voltage, down to almost zero.

2.6.1.1 The inverting topology The output voltage is of the opposite polarity than the input. This is a switchedmode power supply with a similar circuit topology to the boost converter and the buck converter. The output voltage is adjustable based on the duty cycle of the switching transistor. One possible drawback of this converter is that the switch does not have a terminal at ground; this complicates the driving circuitry. Neither drawback is of any consequence if the power supply is isolated from the load circuit (if, for example, the supply is a battery) because the supply and diode polarity can simply be reversed. The switch can be on either the ground side or the supply side.

2.6.1.2 Principle of operation The basic principle of the buck–boost converter is fairly simple (see figure 12): 

While in the On-state, the input voltage source is directly connected to the inductor (L). This results in accumulating energy in L. In this stage, the capacitor supplies energy to the output load.



While in the Off-state, the inductor is connected to the output load and capacitor, so energy is transferred from L to C and R.


21

Fig. 13: The two operating states of a buck–boost converter: When the switch is turned on, the input voltage source supplies current to the inductor, and the capacitor supplies current to the resistor (output load). When the switch is opened, the inductor supplies current to the load via the diode D

Compared to the buck and boost converters, the characteristics of the buck–boost converter are mainly: 

Polarity of the output voltage is opposite to that of the input;



The output voltage can vary continuously from 0 to (for an ideal converter). The output voltage ranges for a buck and a boost converter are respectively to 0 and to

2.6.1.3 Continuous mode If the current through the inductor L never falls to zero during a commutation cycle, the converter is said to operate in continuous mode. The current and voltage waveforms in an ideal converter can be seen in Figure 3. From to , the converter is in On-State, so the switch S is closed. The rate of change in the inductor current (IL) is therefore given by …7

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At the end of the On-state, the increase of IL is therefore: …8

Fig 14: Waveforms of current and voltage in a buck–boost converter operating in continuous mode.

D is the duty cycle. It represents the fraction of the commutation period T during which the switch is On. Therefore D ranges between 0 (S is never on) and 1 (S is always on). During the Off-state, the switch S is open, so the inductor current flows through the load. If we assume zero voltage drops in the diode, and a capacitor large enough for its voltage to remain constant, the evolution of IL is: …9


23

Therefore, the variation of IL during the Off-period is: …10 As we consider that the converter operates in steady-state conditions, the amount of energy stored in each of its components has to be the same at the beginning and at the end of a commutation cycle. As the energy in an inductor is given by: …11 It is obvious that the value of IL at the end of the Off state must be the same with the value of IL at the beginning of the On-state, i.e. the sum of the variations of IL during the on and the off states must be zero: …12 Substituting

and

by their expressions yields: …13

This can be written as: …14 This in return yields that: …15 From the above expression it can be seen that the polarity of the output voltage is always negative (because the duty cycle goes from 0 to 1), and that its absolute value increases with D, theoretically up to minus infinity when D approaches 1. Apart from the polarity, this converter is either step-up (a boost converter) or step-down (a buck converter). Thus it is named a buck–boost converter.

2.6.1.4 Discontinuous mode In some cases, the amount of energy required by the load is small enough to be transferred in a time smaller than the whole commutation period. In this case, the current through the inductor falls to zero during part of the period. The only

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difference in the principle described above is that the inductor is completely discharged at the end of the commutation cycle (see waveforms in figure 4). Although slight, the difference has a strong effect on the output voltage equation. It can be calculated as follows: Because the inductor current at the beginning of the cycle is zero, its maximum value

(at

) is …16

Fig 15: Waveforms of current and voltage in a buck–boost converter operating in discontinuous mode. During the off-period, IL falls to zero after δ.T: …17


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Using the two previous equations, δ is: …18 The load current is equal to the average diode current ( ). As can be seen on figure 4, the diode current is equal to the inductor current during the off-state. Therefore, the output current can be written as: …19 Replacing

and δ by their respective expressions yields: …20

Therefore, the output voltage gain can be written as: …21 Compared to the expression of the output voltage gain for the continuous mode, this expression is much more complicated. Furthermore, in discontinuous operation, the output voltage not only depends on the duty cycle, but also on the inductor value, the input voltage and the output current.

2.6.2

Cuk Converter

The Ćuk converter is a type of DC/DC converter that has an output voltage magnitude that is either greater than or less than the input voltage magnitude. It is essentially a boost converter followed by a buck converter with a capacitor to couple the energy. The non-isolated Ćuk converter can only have opposite polarity between input and output. It uses a capacitor as its main energy-storage component, unlike most other types of converters which use an inductor. It is named after Slobodan Ćuk of the California Institute of Technology, who first presented the design.

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Fig 16: Schematic of a non-isolated Ćuk converter.

2.6.2.1 Operating principle A non-isolated Ćuk converter comprises two inductors, two capacitors, a switch (usually a transistor), and a diode. Its schematic can be seen in figure 15. It is an inverting converter, so the output voltage is negative with respect to the input voltage. The capacitor C is used to transfer energy and is connected alternately to the input and to the output of the converter via the commutation of the transistor and the diode (see figure 16).

Fig 17: The two operating states of a non-isolated Ćuk converter. In this figure, the diode and the switch are either replaced by a short circuit when they are on or by an open circuit when they are off. It can be seen that when in the off-state, the capacitor C is being charged by the input source through the inductor L1. When in the on-state, the capacitor C transfers the energy to the output capacitor through the inductance L2.


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The two inductors L1 and L2 are used to convert respectively the input voltage source (Vi) and the output voltage source (Co) into current sources. At a short time scale an inductor can be considered as a current source as it maintains a constant current. This conversion is necessary because if the capacitor were connected directly to the voltage source, the current would be limited only by the parasitic resistance, resulting in high energy loss. Charging a capacitor with a current source (the inductor) prevents resistive current limiting and its associated energy loss. As with other converters (buck converter, boost converter, buck-boost converter) the Ćuk converter can either operate in continuous or discontinuous current mode. However, unlike these converters, it can also operate in discontinuous voltage mode (the voltage across the capacitor drops to zero during the commutation cycle).

2.6.2.2 Continuous mode In steady state, the energy stored in the inductors has to remain the same at the beginning and at the end of a commutation cycle. The energy in an inductor is given by: …22 This implies that the current through the inductors has to be the same at the beginning and the end of the commutation cycle. As the evolution of the current through an inductor is related to the voltage across it: …23 It can be seen that the average value of the inductor voltages over a commutation period have to be zero to satisfy the steady-state requirements. If we consider that the capacitors C and Co are large enough for the voltage ripple across them to be negligible, the inductor voltages become: 

In the off-state, inductor L1 is connected in series with Vi and C (see figure 16). Therefore . As the diode D is forward biased (we consider zero voltage drop), L2 is directly connected to the output capacitor. Therefore .



In the on-state, inductor L1 is directly connected to the input source.

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Control Strategies For Hybrid Renewable Energy Systems Therefore capacitor, so

. Inductor L2 is connected in series with C and the output .

The converter operates in on state from t=0 to t=D·T (D is the duty cycle), and in off state from D·T to T (that is, during a period equal to (1-D)·T). The average values of VL1 and VL2 are therefore: …24 …25 As both average voltages have to be zero to satisfy the steady-state conditions, using the last equation we can write: …27 So the average voltage across L1 becomes: …28 Which can be written as: …29 It can be seen that this relation is the same as that obtained for the buck-boost converter.

2.6.2.3 Discontinuous mode Like all DC/DC converters Ćuk converters rely on the ability of the inductors in the circuit to provide continuous current, in much the same way a capacitor in a rectifier filter provides continuous voltage. If this inductor is too small or below the "critical inductance", then the current will be discontinuous. This state of operation is usually not studied in much depth, as it is not used beyond a demonstrating of why the minimum inductance is crucial. The minimum inductance is given by: …30 Where

is the switching frequency.

Control Strategies For Hybrid Renewable Energy Systems 2.6.3

29

SEPIC CONVERTER

The Single-Ended Primary-Inductor Converter (SEPIC) is a type of DC/DC converter allowing the electrical potential (voltage) at its output to be greater than, less than, or equal to that at its input. The output of the SEPIC is controlled by the duty cycle of the control transistor. A SEPIC is essentially a boost converter followed by a buck-boost converter, therefore it is similar to a traditional buck-boost converter, but has advantages of having non-inverted output (the output has the same voltage polarity as the input), using a series capacitor to couple energy from the input to the output (and thus can respond more gracefully to a short-circuit output), and being capable of true shutdown: when the switch is turned off, its output drops to 0 V, following a fairly hefty transient dump of charge.

2.6.3.1 Circuit operation The schematic diagram for a basic SEPIC is shown in Figure 17. As with other switched mode power supplies (specifically DC-to-DC converters), the SEPIC exchanges energy between the capacitors and inductors in order to convert from one voltage to another. The amount of energy exchanged is controlled by switch S1, which is typically a transistor such as a MOSFET. MOSFETs offer much higher input impedance and lower voltage drop than bipolar junction transistors (BJTs), and do not require biasing resistors as MOSFET switching is controlled by differences in voltage rather than a current, as with BJTs).

Fig 18: Sepic converter

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2.6.3.2 Continuous mode A SEPIC is said to be in continuous-conduction mode ("continuous mode") if the current through the inductor L1 never falls to zero. During a SEPIC's steadystate operation, the average voltage across capacitor C1 (VC1) is equal to the input voltage (Vin). Because capacitor C1 blocks direct current (DC), the average current through it (IC1) is zero, making inductor L2 the only source of DC load current. Therefore, the average current through inductor L2 (IL2) is the same as the average load current and hence independent of the input voltage. Looking at average voltages, the following can be written: …31 Because the average voltage of VC1 is equal to VIN, VL1 = −VL2. For this reason, the two inductors can be wound on the same core. Since the voltages are the same in magnitude, their effects of the mutual inductance will be zero, assuming the polarity of the windings is correct. Also, since the voltages are the same in magnitude, the ripple currents from the two inductors will be equal in magnitude. The average currents can be summed as follows (average capacitor currents must be zero): …32 When switch S1 is turned on, current IL1 increases and the current IL2 goes more negative. (Mathematically, it decreases due to arrow direction.) The energy to increase the current IL1 comes from the input source. Since S1 is a short while closed, and the instantaneous voltage VC1 is approximately VIN, the voltage VL2 is approximately −VIN. Therefore, the capacitor C1 supplies the energy to increase the magnitude of the current in IL2 and thus increase the energy stored in L2. The easiest way to visualize this is to consider the bias voltages of the circuit in a d.c. state, then close S1.

Figure 19: With S1 closed current increases through L1 (green) and C1 discharges increasing current in L2 (red)


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When switch S1 is turned off, the current IC1 becomes the same as the current IL1, since inductors do not allow instantaneous changes in current. The current IL2 will continue in the negative direction, in fact it never reverses direction. It can be seen from the diagram that a negative IL2 will add to the current IL1 to increase the current delivered to the load. Using Kirchhoff's Current Law, it can be shown that ID1 = IC1 - IL2. It can then be concluded, that while S1 is off, power is delivered to the load from both L2 and L1. C1, however is being charged by L1 during this off cycle, and will in turn recharge L2 during the on cycle.

Figure 20: With S1 open current through L1 (green) and current through L2 (red) produce current through the load

Because the potential (voltage) across capacitor C1 may reverse direction every cycle, a non-polarized capacitor should be used. However, a polarized tantalum or electrolytic capacitor may be used in some cases, because the potential (Voltage) across capacitor C1 will not change unless the switch is closed long enough for a half cycle of resonance with inductor L2, and by this time the current in inductor L1 could be quite large. The capacitor CIN is required to reduce the effects of the parasitic inductance and internal resistance of the power supply. The boost/buck capabilities of the SEPIC are possible because of capacitor C1 and inductor L2. Inductor L1 and switch S1 create a standard boost converter, which generates a voltage (VS1) that is higher than VIN, whose magnitude is determined by the duty cycle of the switch S1. Since the average voltage across C1 is VIN, the output voltage (VO) is VS1 - VIN. If VS1 is less than double VIN, then the output voltage will be less than the input voltage. If VS1 is greater than double VIN, then the output voltage will be greater than the input voltage.

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The evolution of switched-power supplies can be seen by coupling the two inductors in a SEPIC converter together, which begins to resemble a Flyback converter, the most basic of the transformer-isolated SMPS topologies.

2.6.3.3 Reliability and Efficiency The voltage drop and switching time of diode D1 is critical to a SEPIC's reliability and efficiency. The diode's switching time needs to be extremely fast in order to not generate high voltage spikes across the inductors, which could cause damage to components. Fast conventional diodes or Schottky diodes may be used. The resistances in the inductors and the capacitors can also have large effects on the converter efficiency and ripple. Inductors with lower series resistance allow less energy to be dissipated as heat, resulting in greater efficiency (a larger portion of the input power being transferred to the load). Capacitors with low equivalent series resistance (ESR) should also be used for C1 and C2 to minimize ripple and prevent heat build-up, especially in C1 where the current is changing direction frequently.

2.7

PWM control

Pulse-width modulation (PWM), or pulse-duration modulation (PDM), is a modulation technique used to encode a message into a pulsing signal. Although this modulation technique can be used to encode information for transmission, its main use is to allow the control of the power supplied to electrical devices, especially to inertial loads such as motors. In addition, PWM is one of the two principal algorithms used in photovoltaic solar battery chargers, the other being MPPT. The average value of voltage (and current) fed to the load is controlled by turning the switch between supply and load on and off at a fast rate. The longer the switch is on compared to the off periods, the higher the total power supplied to the load. The PWM switching frequency has to be much higher than what would affect the load (the device that uses the power), which is to say that the resultant waveform perceived by the load must be as smooth as possible. Typically switching has to be done several times a minute in an electric stove, 120 Hz in a lamp dimmer, from few kilohertz (kHz) to tens of kHz for a motor drive and well into the tens


33

or hundreds of kHz in audio amplifiers and computer power supplies. The term duty cycle describes the proportion of 'on' time to the regular interval or 'period' of time; a low duty cycle corresponds to low power, because the power is off for most of the time. Duty cycle is expressed in percent, 100% being fully on. The main advantage of PWM is that power loss in the switching devices is very low. When a switch is off there is practically no current, and when it is on and power is being transferred to the load, there is almost no voltage drop across the switch. Power loss, being the product of voltage and current, is thus in both cases close to zero. PWM also works well with digital controls, which, because of their on/off nature, can easily set the needed duty cycle. PWM has also been used in certain communication systems where its duty cycle has been used to convey information over a communications channel.

2.7.1

Principle

Pulse-width modulation uses a rectangular pulse wave whose pulse width is modulated resulting in the variation of the average value of the waveform. If we consider a pulse waveform , with period , low value , a high value and a duty cycle D (see figure 1), the average value of the waveform is given by: …33 As

is a pulse wave, its value is

for

and

. The above expression then becomes:

…34 This latter expression can be fairly simplified in many cases where as

.

From this, it is obvious that the average value of the signal ( ) is directly dependent on the duty cycle D. This latter expression can be fairly simplified in many cases where

as

. From this, it is obvious that

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the average value of the signal ( ) is directly dependent on the duty cycle D.

Fig 21: a pulse wave, showing the definitions of

,

and D.

The simplest way to generate a PWM signal is the intersective method, which requires only a sawtooth or a triangle waveform (easily generated using a simple oscillator) and a comparator. When the value of the reference signal (the red sine wave in figure 2) is more than the modulation waveform (blue), the PWM signal (magenta) is in the high state, otherwise it is in the low state.

Fig. 21: A simple method to generate the PWM pulse train corresponding to a given signal is the intersective PWM: the signal (here the red sinewave) is compared with a sawtooth waveform (blue). When the latter is less than the former, the PWM signal (magenta) is in high state (1). Otherwise it is in the low state (0).


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2.7.1.1 Delta modulation In the use of delta modulation for PWM control, the output signal is integrated, and the result is compared with limits, which correspond to a Reference signal offset by a constant. Every time the integral of the output signal reaches one of the limits, the PWM signal changes state. Figure 3

Fig 22: Principle of the delta PWM. The output signal (blue) is compared with the limits (green). These limits correspond to the reference signal (red), offset by a given value. Every time the output signal (blue) reaches one of the limits, the PWM signal changes state.

2.7.1.2 Delta-sigma modulation In delta-sigma modulation as a PWM control method, the output signal is subtracted from a reference signal to form an error signal. This error is integrated, and when the integral of the error exceeds the limits, the output changes state. Figure 23

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Fig 23: Principle of the sigma-delta PWM. The top green waveform is the reference signal, on which the output signal (PWM, in the bottom plot) is subtracted to form the error signal (blue, in top plot). This error is integrated (middle plot), and when the integral of the error exceeds the limits (red lines), the output changes state.

2.7.2

Space vector modulation

Space vector modulation is a PWM control algorithm for multi-phase AC generation, in which the reference signal is sampled regularly; after each sample, non-zero active switching vectors adjacent to the reference vector and one or more of the zero switching vectors are selected for the appropriate fraction of the sampling period in order to synthesize the reference signal as the average of the used vectors.

2.7.3

Direct torque control (DTC)

Direct torque control is a method used to control AC motors. It is closely related with the delta modulation (see above). Motor torque and magnetic flux are estimated and these are controlled to stay within their hysteresis bands by turning on new combination of the device's semiconductor switches each time either of the signal tries to deviate out of the band.


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Time proportioning

Many digital circuits can generate PWM signals (e.g., many microcontrollers have PWM outputs). They normally use a counter that increments periodically (it is connected directly or indirectly to the clock of the circuit) and is reset at the end of every period of the PWM. When the counter value is more than the reference value, the PWM output changes state from high to low (or low to high). This technique is referred to as time proportioning, particularly as time-proportioning control[4] – which proportion of a fixed cycle time is spent in the high state. The incremented and periodically reset counter is the discrete version of the intersecting method's sawtooth. The analog comparator of the intersecting method becomes a simple integer comparison between the current counter value and the digital (possibly digitized) reference value. The duty cycle can only be varied in discrete steps, as a function of the counter resolution. However, a highresolution counter can provide quite satisfactory performance.

2.7.5

Types

Three types of pulse-width modulation (PWM) are possible: 1. The pulse center may be fixed in the center of the time window and both edges of the pulse moved to compress or expand the width. 2. The lead edge can be held at the lead edge of the window and the tail edge modulated. 3. The tail edge can be fixed and the lead edge modulated.

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Fig 24 : Three types of PWM signals (blue): leading edge modulation (top), trailing edge modulation (middle) and centered pulses (both edges are modulated, bottom). The green lines are the sawtooth waveform (first and second cases) and a triangle waveform (third case) used to generate the PWM waveforms using the intersective method.

2.7.6

Spectrum

The resulting spectra (of the three cases) are similar, and each contains a dc component—a base sideband containing the modulating signal and phase modulated carriers at each harmonic of the frequency of the pulse. The amplitudes of the harmonic groups are restricted by a envelope (sin c function) and extend to infinity. The infinite bandwidth is caused by the nonlinear operation of the pulse-width modulator. In consequence, a digital PWM suffers from aliasing distortion that significantly reduces its applicability for modern communications system. By limiting the bandwidth of the PWM kernel, aliasing effects can be avoided. On the contrary, the delta modulation is a random process that produces continuous spectrum without distinct harmonics.


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PWM sampling theorem

The process of PWM conversion is non-linear and it is generally supposed that low pass filter signal recovery is imperfect for PWM. The PWM sampling theorem shows that PWM conversion can be perfect. The theorem states that "Any bandlimited baseband signal within ±0.637 can be represented by a pulse width modulation (PWM) waveform with unit amplitude. The number of pulses in the waveform is equal to the number of Nyquist samples and the peak constraint is independent of whether the waveform is two-level or three-level." • Sampling Theorem: “A bandlimited signal can be reconstructed exactly if it is sampled at a rate atleast twice the maximum frequency component in it.”

2.7.8

Applications

2.7.8.1 Servos PWM is used to control servomechanisms.

2.7.8.2 Telecommunications In telecommunications, PWM is a form of signal modulation where the widths of the pulses correspond to specific data values encoded at one end and decoded at the other. Pulses of various lengths (the information itself) will be sent at regular intervals (the carrier frequency of the modulation). The inclusion of a clock signal is not necessary, as the leading edge of the data signal can be used as the clock if a small offset is added to the data value in order to avoid a data value with a zero length pulse.

2.7.8.3 Power delivery PWM can be used to control the amount of power delivered to a load without incurring the losses that would result from linear power delivery by resistive means. Drawbacks to this technique are that the power drawn by the load is not constant but rather discontinuous (see Buck converter), and energy delivered to the load is not continuous either. However, the load may be inductive, and with a sufficiently high frequency and when necessary using additional passive electronic filters, the pulse train can be smoothed and average analog

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waveform recovered. Power flow into the load can be continuous. Power flow from the supply is not constant and will require energy storage on the supply side in most cases. (In the case of an electrical circuit, a capacitor to absorb energy stored in (often parasitic) supply side inductance.) High frequency PWM power control systems are easily realisable with semiconductor switches. As explained above, almost no power is dissipated by the switch in either on or off state. However, during the transitions between on and off states, both voltage and current are nonzero and thus power is dissipated in the switches. By quickly changing the state between fully on and fully off (typically less than 100 nanoseconds), the power dissipation in the switches can be quite low compared to the power being delivered to the load. Modern semiconductor switches such as MOSFETs or Insulated-gate bipolar transistors (IGBTs) are well suited components for high-efficiency controllers. Frequency converters used to control AC motors may have efficiencies exceeding 98%. Switching power supplies have lower efficiency due to low output voltage levels (often even less than 2 V for microprocessors are needed) but still more than 70–80% efficiency can be achieved. Variable-speed fan controllers for computers usually use PWM, as it is far more efficient when compared to a potentiometer or rheostat. (Neither of the latter is practical to operate electronically; they would require a small drive motor.) Light dimmers for home use employ a specific type of PWM control. Home-use light dimmers typically include electronic circuitry which suppresses current flow during defined portions of each cycle of the AC line voltage. Adjusting the brightness of light emitted by a light source is then merely a matter of setting at what voltage (or phase) in the AC halfcycle the dimmer begins to provide electric current to the light source (e.g. by using an electronic switch such as a triac). In this case the PWM duty cycle is the ratio of the conduction time to the duration of the half AC cycle defined by the frequency of the AC line voltage (50 Hz or 60 Hz depending on the country). These rather simple types of dimmers can be effectively used with inert (or relatively slow reacting) light sources such as incandescent lamps, for example, for which the additional modulation in supplied electrical energy which is caused by the dimmer causes only negligible additional fluctuations in the emitted light. Some other types of light sources such as light-emitting diodes (LEDs), however, turn on and off extremely rapidly and would perceivably flicker if supplied with low frequency drive voltages. Perceivable flicker effects from such rapid response light sources can be reduced by increasing the PWM frequency. If the


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light fluctuations are sufficiently rapid, the human visual system can no longer resolve them and the eye perceives the time average intensity without flicker (see flicker fusion threshold). In electric cookers, continuously variable power is applied to the heating elements such as the hob or the grill using a device known as a Simmerstat. This consists of a thermal oscillator running at approximately two cycles per minute and the mechanism varies the duty cycle according to the knob setting. The thermal time constant of the heating elements is several minutes, so that the temperature fluctuations are too small to matter in practice.

2.7.8.4 Voltage regulation PWM is also used in efficient voltage regulators. By switching voltage to the load with the appropriate duty cycle, the output will approximate a voltage at the desired level. The switching noise is usually filtered with an inductor and a capacitor. One method measures the output voltage. When it is lower than the desired voltage, it turns on the switch. When the output voltage is above the desired voltage, it turns off the switch.

2.7.8.5 Audio effects and amplification PWM is sometimes used in sound (music) synthesis, in particular subtractive synthesis, as it gives a sound effect similar to chorus or slightly detuned oscillators played together. (In fact, PWM is equivalent to the difference of two sawtooth waves with one of them inverted.) The ratio between the high and low level is typically modulated with a low frequency oscillator. In addition, varying the duty cycle of a pulse waveform in a subtractive-synthesis instrument creates useful timbral variations. Some synthesizers have a duty-cycle trimmer for their square-wave outputs, and that trimmer can be set by ear; the 50% point (true square wave) was distinctive, because even-numbered harmonics essentially disappear at 50%. Pulse waves, usually 50%, 25%, and 12.5%, make up the soundtracks of classic video games. A new class of audio amplifiers based on the PWM principle is becoming popular. Called "Class-D amplifiers", they produce a PWM equivalent of the analog input signal which is fed to the loudspeaker via a suitable filter network to block the carrier and recover the original audio. These amplifiers are

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characterized by very good efficiency figures (≥ 90%) and compact size/light weight for large power outputs. For a few decades, industrial and military PWM amplifiers have been in common use, often for driving servo motors. Fieldgradient coils in MRI machines are driven by relatively high-power PWM amplifiers. Historically, a crude form of PWM has been used to play back PCM digital sound on the PC speaker, which is driven by only two voltage levels, typically 0 V and 5 V. By carefully timing the duration of the pulses, and by relying on the speaker's physical filtering properties (limited frequency response, selfinductance, etc.) it was possible to obtain an approximate playback of mono PCM samples, although at a very low quality, and with greatly varying results between implementations. In more recent times, the Direct Stream Digital sound encoding method was introduced, which uses a generalized form of pulse-width modulation called pulse density modulation, at a high enough sampling rate (typically in the order of MHz) to cover the whole acoustic frequencies range with sufficient fidelity. This method is used in the SACD format, and reproduction of the encoded audio signal is essentially similar to the method used in class-D amplifiers.

2.7.8.6 Electrical SPWM (Sine–triangle pulse width modulation) signals are used in micro-inverter design (used in solar or wind power applications). These switching signals are fed to the FETs that are used in the device. The device's efficiency depends on the harmonic content of the PWM signal. There is much research on eliminating unwanted harmonics and improving the fundamental strength, some of which involves using a modified carrier signal instead of a classic sawtooth signal in order to decrease power losses and improve efficiency. Another common application is in robotics where PWM signals are used to control the speed of the robot by controlling the motors.

2.8

Low pass filter

A low-pass filter is a filter that passes signals with a frequency lower than a certain cutoff frequency and attenuates signals with frequencies higher than the cutoff frequency. The amount of attenuation for each frequency depends on the filter design. The filter is sometimes called a high-cut filter, or treble cut


43

filter in audio applications. A low-pass filter is the opposite of a high-pass filter. A band-pass filter is a combination of a low-pass and a high-pass filter. Low-pass filters exist in many different forms, including electronic circuits (such as a hiss filter used in audio), anti-aliasing filters for conditioning signals prior to analog-to-digital conversion, digital filters for smoothing sets of data, acoustic barriers, blurring of images, and so on. The moving average operation used in fields such as finance is a particular kind of low-pass filter, and can be analyzed with the same signal processing techniques as are used for other low-pass filters. Low-pass filters provide a smoother form of a signal, removing the short-term fluctuations, and leaving the longer-term trend. An optical filter can correctly be called a low-pass filter, but conventionally is called a long pass filter (low frequency is long wavelength), to avoid confusion.

2.8.1

Ideal low pass filter

An ideal low-pass filter completely eliminates all frequencies above the cut off frequency while passing those below unchanged; its frequency response is a rectangular function and is a brick-wall filter. The transition region present in practical filters does not exist in an ideal filter. An ideal low-pass filter can be realized mathematically (theoretically) by multiplying a signal by the rectangular function in the frequency domain or, equivalently, convolution with its impulse response, a sinc function, in the time domain. However, the ideal filter is impossible to realize without also having signals of infinite extent in time, and so generally needs to be approximated for real ongoing signals, because the sinc function's support region extends to all past and future times. The filter would therefore need to have infinite delay, or knowledge of the infinite future and past, in order to perform the convolution. It is effectively realizable for pre-recorded digital signals by assuming extensions of zero into the past and future, or more typically by making the signal repetitive and using Fourier analysis. Real filters for real-time applications approximate the ideal filter by truncating and windowing the infinite impulse response to make a finite impulse response; applying that filter requires delaying the signal for a moderate period of time, allowing the computation to "see" a little bit into the future. This delay is manifested as phase shift. Greater accuracy in approximation requires a longer delay. An ideal low-pass filter results in ringing artifacts via the Gibbs phenomenon.

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These can be reduced or worsened by choice of windowing function, and the design and choice of real filters involves understanding and minimizing these artifacts. For example, "simple truncation [of sinc] causes severe ringing artifacts," in signal reconstruction, and to reduce these artifacts one uses window functions "which drop off more smoothly at the edges." The Whittaker–Shannon interpolation formula describes how to use a perfect low-pass filter to reconstruct a continuous signal from a sampled digital signal. Real digital-to-analog converters use real filter approximations.

Fig 25: The sinc function, the impulse response of an ideal low-pass filter.

2.8.2

Continuous–time low pass filter

There are many different types of filter circuits, with different responses to changing frequency. The frequency response of a filter is generally represented using a Bode plot, and the filter is characterized by its cut off frequency and rate of frequency rol loff. In all cases, at the cut off frequency, the filter attenuates the input power by half or 3 dB. So the order of the filter determines the amount of additional attenuation for frequencies higher than the cut off frequency. 

A first-order filter, for example, reduces the signal amplitude by half (so power reduces by a factor of 4, or 6 dB), every time the frequency doubles (goes up one octave); more precisely, the power roll off approaches 20 dB per decade in the limit of high frequency. The magnitude Bode plot for a first-order filter looks like a horizontal line below the cutoff frequency, and a diagonal line above the cutoff frequency. There is also a "knee curve" at the


45

boundary between the two, which smoothly transitions between the two straight line regions. If the transfer function of a first-order low-pass filter has a zero as well as a pole, the Bode plot flattens out again, at some maximum attenuation of high frequencies; such an effect is caused for example by a little bit of the input leaking around the one-pole filter; this one-pole–one-zero filter is still a first-order low-pass. See Pole–zero plot and RC circuit. 

A second-order filter attenuates higher frequencies more steeply. The Bode plot for this type of filter resembles that of a first-order filter, except that it falls off more quickly. For example, a second-order Butterworth filter reduces the signal amplitude to one fourth its original level every time the frequency doubles (so power decreases by 12 dB per octave, or 40 dB per decade). Other all-pole second-order filters may roll off at different rates initially depending on their Q factor, but approach the same final rate of 12 dB per octave; as with the first-order filters, zeroes in the transfer function can change the high-frequency asymptote. See RLC circuit.



Third- and higher-order filters are defined similarly. In general, the final rate of power roll off for an order- all-pole filter is dB per octave (i.e., dB per decade).

Fig 26: The gain-magnitude frequency response of a first-order (onepole) low-pass filter. Power gain is shown in decibels (i.e., a 3 dB decline reflects an additional half-power attenuation).Angular frequency is shown on a logarithmic scale in units of radians per second.

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On any Butterworth filter, if one extends the horizontal line to the right and the diagonal line to the upper-left (the asymptotes of the function), they intersect at exactly the cut off frequency. The frequency response at the cut off frequency in a first-order filter is 3 dB below the horizontal line. The various types of filters (Butterworth filter, Chebyshev filter, Bessel filter, etc.) all have differentlooking knee curves. Many second-order filters have "peaking" or resonance that puts their frequency response at the cut off frequency above the horizontal line. Furthermore, the actual frequency where this peaking occurs can be predicted without calculus, as shown by Cartwright et al. For third-order filters, the peaking and its frequency of occurrence can too be predicted without calculus as recently shown by Cartwright et al. See electronic filter for other types. The meanings of 'low' and 'high' that is, the cut off frequency depend on the characteristics of the filter. The term "low-pass filter" merely refers to the shape of the filter's response; a high-pass filter could be built that cuts off at a lower frequency than any low-pass filter it is their responses that set them apart. Electronic circuits can be devised for any desired frequency range, right up through microwave frequencies (above 1 GHz) and higher.

2.8.3

Laplace notation

Continuous-time filters can also be described in terms of the Laplace transform of their impulse response, in a way that lets all characteristics of the filter be easily analyzed by considering the pattern of poles and zeros of the Laplace transform in the complex plane. (In discrete time, one can similarly consider the Z-transform of the impulse response.) For example, a first-order low-pass filter can be described in Laplace notation as: …35 where s is the Laplace transform variable, τ is the filter time constant, and K is the gain of the filter in the passband .

2.8.4

Electronic low pass filter

2.8.4.1 First Order 2.8.4.1.1 RC filter One simple low-pass filter circuit consists of a resistor in series with a load, and


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a capacitor in parallel with the load. The capacitor exhibits reactance, and blocks low-frequency signals, forcing them through the load instead. At higher frequencies the reactance drops, and the capacitor effectively functions as a short circuit. The combination of resistance and capacitance gives the time constant of the filter (represented by the Greek letter tau).

Fig 27: Passive, first order low-pass RC filter

The break frequency, also called the turnover frequency or cut off frequency (in hertz), is determined by the time constant: …36

or equivalently (in radians per second): …37 This circuit may be understood by considering the time the capacitor needs to charge or discharge through the resistor: 

At low frequencies, there is plenty of time for the capacitor to charge up to practically the same voltage as the input voltage.



At high frequencies, the capacitor only has time to charge up a small amount before the input switches direction. The output goes up and down only a small fraction of the amount the input goes up and down. At double the frequency, there's only time for it to charge up half the amount.

Another way to understand this circuit is through the concept of reactance at a particular frequency:

48

Control Strategies For Hybrid Renewable Energy Systems 

Since direct current (DC) cannot flow through the capacitor, DC input must flow out the path marked (analogous to removing the capacitor).



Since alternating current (AC) flows very well through the capacitor, almost as well as it flows through solid wire, AC input flows out through the capacitor, effectively short circuiting to ground (analogous to replacing the capacitor with just a wire).

The capacitor is not an "on/off" object (like the block or pass fluidic explanation above). The capacitor variably acts between these two extremes. It is the Bode plot and frequency response that show this variability.

2.8.4.1.2

RL filter

A resistor–inductor circuit or RL filter is an electric circuit composed of resistors and inductors driven by a voltage or current source. A first order RL circuit is composed of one resistor and one inductor and is the simplest type of RL circuit. A first order RL circuit is one of the simplest analogue infinite impulse response electronic filters. It consists of a resistor and an inductor, either in series driven by a voltage source or in parallel driven by a current source.

2.8.4.2 Second order 2.8.4.2.1

RLC filter

An RLC circuit (the letters R, L and C can be in other orders) is an electrical circuit consisting of a resistor, an inductor, and a capacitor, connected in series or in parallel. The RLC part of the name is due to those letters being the usual electrical symbols for resistance, inductance and capacitance respectively. The circuit forms a harmonic oscillator for current and will resonate in a similar way as an LC circuit will. The main difference that the presence of the resistor makes is that any oscillation induced in the circuit will die away over time if it is not kept going by a source. This effect of the resistor is called damping. The presence of the resistance also reduces the peak resonant frequency somewhat. Some resistance is unavoidable in real circuits, even if a resistor is not specifically included as a component. An ideal, pure LC circuit is an abstraction for the purpose of theory.


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Fig 28: RLC circuit as a low-pass filter

There are many applications for this circuit. They are used in many different types of oscillator circuits. Another important application is fortuning, such as in radio receivers or television sets, where they are used to select a narrow range of frequencies from the ambient radio waves. In this role the circuit is often referred to as a tuned circuit. An RLC circuit can be used as a band-pass filter, band-stop filter, low-pass filter or high-pass filter. The RLC filter is described as a second-order circuit, meaning that any voltage or current in the circuit can be described by a second-order differential equation in circuit analysis.

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CHAPTER - 3

CONTROLLER

3.1 PROPORTIONAL INTEGRAL DERIVATIVE (PID) CONTROLLER A proportional–integral–derivative controller (PID controller) is a control loop feedback mechanism (controller) commonly used in industrial control systems. A PID controller continuously calculates an error value as the difference between a measured process variable and a desired set point. The controller attempts to minimize the error over time by adjustment of a control variable, such as the position of a control valve, a damper, or the power supplied to a heating element, to a new value determined by a weighted sum: …38 where , , and , all non-negative, denote the coefficients for the proportional, integral, and derivative terms, respectively (sometimes denoted P, I, and D). In this model, 

P accounts for present values of the error (e.g. if the error is large and positive, the control variable will be large and negative),



I accounts for past values of the error (e.g. if the output is not sufficient to reduce the size of the error, the control variable will accumulate over time, causing the controller to apply a stronger action), and



D accounts for possible future values of the error, based on its current rate of change.[1]

As a PID controller relies only on the measured process variable, not on knowledge of the underlying process, it is broadly applicable. By tuning the three parameters of the model, a PID controller can deal with specific process requirements. The response of the controller can be described in terms of its responsiveness to an error, the degree to which the system overshoots a setpoint, and the degree of any system oscillation. The use of the PID algorithm does not guarantee optimal control of the system or even itsstability.

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Control Strategies For Hybrid Renewable Energy Systems Some applications may require using only one or two terms to provide the appropriate system control. This is achieved by setting the other parameters to zero. A PID controller will be called a PI, PD, P or I controller in the absence of the respective control actions. PI controllers are fairly common, since derivative action is sensitive to measurement noise, whereas the absence of an integral term may prevent the system from reaching its target value. For discrete time systems, the term PSD, for proportional-summationdifference, is often used

Fig 29: A block diagram of a PID controller in a feedback loop

3.1.1

Control loop basics

A robotic arm can be moved and positioned by a control loop. By applying forward and reverse power to an electric motor to lift and lower the arm, it may be necessary to allow for the inertial mass of the arm, forces due to gravity, and to correct for external forces on the arm such as a load to lift or work to be done on an external object. The sensed position is the process variable (PV). The desired position is called the setpoint (SP). The input to the process (the electric current in the motor) is the output from the PID controller. It is called either the manipulated variable (MV) or the control variable (CV). The difference between the present position and the set point is the error (e), which quantifies whether the arm is too low or too high and by how much.


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By measuring the position (PV), and subtracting it from the set point (SP), the error (e) is found, and from it the controller calculates how much electric current to supply to the motor (MV). The obvious method is proportional control: the motor current is set in proportion to the existing error. A more complex control may include another term: derivative action. This considers the rate of change of error, supplying more or less electric current depending on how fast the error is approaching zero. Finally, integral action adds a third term, using the accumulated position error in the past to detect whether the position of the mechanical arm is settling out too low or too high and to set the electrical current in relation not only to the error but also the time for which it has persisted. An alternative formulation of integral action is to change the electric current in small persistent steps that are proportional to the current error. Over time the steps accumulate and add up dependent on past errors; this is the discretetime equivalent to integration. In the interest of achieving a controlled arrival at the desired position (SP) in a timely and accurate way, the controlled system needs to be critically damped. A well-tuned position control system will also apply the necessary currents to the controlled motor so that the arm pushes and pulls as necessary to resist external forces trying to move it away from the required position. The set point itself may be generated by an external system, such as a PLC or other computer system, so that it continuously varies depending on the work that the robotic arm is expected to do. A well-tuned PID control system will enable the arm to meet these changing requirements to the best of its capabilities. If a controller starts from a stable state with zero error (PV = SP), then further changes by the controller will be in response to changes in other measured or unmeasured inputs to the process that affect the process, and hence the PV. Variables that affect the process other than the MV are known as disturbances. Generally controllers are used to reject disturbances and to implement set point changes. A change in load on the arm constitutes a disturbance to the robot arm control process. In theory, a controller can be used to control any process which has a measurable output (PV), a known ideal value for that output (SP) and an input to the process (MV) that will affect the relevant PV. Controllers are used in industry to regulate temperature, pressure, force, feed, flowrate, chemical composition, weig ht, position, speed and practically every other variable for which a measurement exists.

54 3.1.2

Control Strategies For Hybrid Renewable Energy Systems PID controller theory

This section describes the parallel or non-interacting form of the PID controller. For other forms please see the section Alternative nomenclature and PID forms. The PID control scheme is named after its three correcting terms, whose sum constitutes the manipulated variable (MV). The proportional, integral, and derivative terms are summed to calculate the output of the PID controller. Defining as the controller output, the final form of the PID algorithm is: …39 where, : Proportional gain, a tuning parameter : Integral gain, a tuning parameter : Derivative gain, a tuning parameter : Error : Time or instantaneous time (the present) : Variable of integration; takes on values from time 0 to the present .

Equivalently, the transfer function in the Laplace Domain of the PID controller is …40 where, : complex number frequency


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3.1.2.1 Proportional term

Fig 30: Plot of PV vs time, for three values of Kp (Ki and Kdheld constant)

The contribution from the integral term is proportional to both the magnitude of the error and the duration of the error. The integral in a PID controller is the sum of the instantaneous error over time and gives the accumulated offset that should have been corrected previously. The accumulated error is then multiplied by the integral gain ( ) and added to the controller output. The integral term is given by: …41

The integral term accelerates the movement of the process towards set point and eliminates the residual steady-state error that occurs with a pure proportional controller. However, since the integral term responds to accumulated errors from the past, it can cause the present value to overshoot the set point value (see the section on loop tuning).

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Fig 31: Plot of PV vs time, for three values of Ki (Kp and Kd held constant)

3.1.2.2 Derivative term

Fig 32: Plot of PV vs time, for three values of Kd (Kp and Ki held constant)

The derivative of the process error is calculated by determining the slope of the error over time and multiplying this rate of change by the derivative gain Kd. The


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magnitude of the contribution of the derivative term to the overall control action is termed the derivative gain, Kd. The derivative term is given by: …42 Derivative action predicts system behaviour and thus improves settling time and stability of the system. An ideal derivative is not causal, so that implementations of PID controllers include an additional low pass filtering for the derivative term, to limit the high frequency gain and noise. Derivative action is seldom used in practice though - by one estimate in only 25% of deployed controllers - because of its variable impact on system stability in real-world applications

3.1.2.3 Stability If the PID controller parameters (the gains of the proportional, integral and derivative terms) are chosen incorrectly, the controlled process input can be unstable, i.e., its output diverges, with or without oscillation, and is limited only by saturation or mechanical breakage. Instability is caused by excess gain, particularly in the presence of significant lag. Generally, stabilization of response is required and the process must not oscillate for any combination of process conditions and set points, though sometimes marginal stability (bounded oscillation) is acceptable or desired. Mathematically, the origins of instability can be seen in the Laplace domain. The total loop transfer function is: …43 where : PID transfer function : Plant transfer function The system is called unstable where the closed loop transfer function diverges for some . This happens for situations where . Typically, this happens when with a 180 degree phase shift. Stability is guaranteed when for frequencies that suffer high phase shifts. A more

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general formalism of this effect is known as the Nyquist stability criterion.

3.1.3

Optimum behaviour

The optimum behaviour on a process change or set point change varies depending on the application. Two basic requirements are regulation (disturbance rejection staying at a given set point) and command tracking (implementing set point changes) – these refer to how well the controlled variable tracks the desired value. Specific criteria for command tracking include rise time and settling time. Some processes must not allow an overshoot of the process variable beyond the set point if, for example, this would be unsafe. Other processes must minimize the energy expended in reaching a new set point.

3.1.4

Overview of methods

There are several methods for tuning a PID loop. The most effective methods generally involve the development of some form of process model, then choosing P, I, and D based on the dynamic model parameters. Manual tuning methods can be relatively time consuming, particularly for systems with long loop times. The choice of method will depend largely on whether or not the loop can be taken "offline" for tuning, and on the response time of the system. the best tuning method often involves subjecting the system to a step change in input, measuring the output as a function of time, and using this response to determine the control parameters.


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Table 2: Choosing a tuning method Method

Advantages

Disadvantages

Manual tuning

No math required; online.

Requires personnel.

Ziegler– Nichols[a]

Proven method; online.

Process upset, some trialand-error, very aggressive tuning.

Tyreus Luyben

Proven method; online.

Process upset, some trialand-error, very aggressive tuning.

Software tools

Consistent tuning; online or offline can employ computer-automated control system design (CAutoD) Some cost or training techniques; may include valve and involved. sensor analysis; allows simulation before downloading; can support nonsteady-state (NSS) tuning.

experienced

Some math; offline; only good for first-order processes.

Cohen– Coon

Good process models.

ÅströmHägglund

Can be used for auto tuning; amplitude The process itself is minimum so this method has lowest inherently oscillatory. process upset

3.1.5

is

Manual tuning

If the system must remain online, one tuning method is to first set

and

values to zero. Increase the

until the output of the loop

oscillates, then the should be set to approximately half of that value for a "quarter amplitude decay" type response. Then increase until any offset is corrected in sufficient time for the process. However, too much will cause instability. Finally, increase , if required, until the loop is acceptably quick to reach its reference after a load disturbance. However, too much will cause excessive response and overshoot. A fast PID loop tuning usually

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overshoots slightly to reach the setpoint more quickly; however, some systems cannot accept overshoot, in which case an over-damped closed-loop system is required, which will require a the

setting significantly less than half that of

setting that was causing oscillation.

Table 3: Effects of increasing a parameter independently Parameter Rise time Overshoot

Settling time

Steadystate error

Stability

Decrease

Increase

Small change

Decrease

Degrade

Decrease

Increase

Increase

Eliminate

Degrade

Minor change

Decrease

Decrease

No effect in theory

Improve if

small

3.1.5.1 Ziegler–Nichols method Another heuristic tuning method is formally known as the Ziegler–Nichols method, introduced by John G. Ziegler and Nathaniel B. Nichols in the 1940s. As in the method above, the and gains are first set to zero. The proportional gain is increased until it reaches the ultimate gain, , at which the output of the loop starts to oscillate. and the oscillation period are used to set the gains as shown: Table 4: Ziegler–Nichols method Control Type P PI

-

-

PID These gains apply to the ideal, parallel form of the PID controller. When applied to the standard PID form, the integral and derivative time parameters and are only dependent on the oscillation period . Please see the section "Alternative nomenclature and PID forms".


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PID tuning software

Most modern industrial facilities no longer tune loops using the manual calculation methods shown above. Instead, PID tuning and loop optimization software are used to ensure consistent results. These software packages will gather the data, develop process models, and suggest optimal tuning. Some software packages can even develop tuning by gathering data from reference changes. Mathematical PID loop tuning induces an impulse in the system, and then uses the controlled system's frequency response to design the PID loop values. In loops with response times of several minutes, mathematical loop tuning is recommended, because trial and error can take days just to find a stable set of loop values. Optimal values are harder to find. Some digital loop controllers offer a self-tuning feature in which very small set point changes are sent to the process, allowing the controller itself to calculate optimal tuning values. Other formulas are available to tune the loop according to different performance criteria. Many patented formulas are now embedded within PID tuning software and hardware modules. Advances in automated PID Loop Tuning software also deliver algorithms for tuning PID Loops in a dynamic or Non-Steady State (NSS) scenario. The software will model the dynamics of a process, through a disturbance, and calculate PID control parameters in response.

3.1.7

Limitations of PID control

While PID controllers are applicable to many control problems, and often perform satisfactorily without any improvements or only coarse tuning, they can perform poorly in some applications, and do not in general provide optimal control. The fundamental difficulty with PID control is that it is a feedback system, with constant parameters, and no direct knowledge of the process, and thus overall performance is reactive and a compromise. While PID control is the best controller in an observer without a model of the process,[2]better performance can be obtained by overtly modelling the actor of the process without resorting to an observer. PID controllers, when used alone, can give poor performance when the PID loop gains must be reduced so that the control system does not overshoot, oscillate or hunt about the control set point value. They also have difficulties in the presence of non-linearities, may trade-off regulation versus response time, do not

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react to changing process behaviour (say, the process changes after it has warmed up), and have lag in responding to large disturbances. The most significant improvement is to incorporate feed-forward control with knowledge about the system, and using the PID only to control error. Alternatively, PIDs can be modified in more minor ways, such as by changing the parameters (either gain scheduling in different use cases or adaptively modifying them based on performance), improving measurement (higher sampling rate, precision, and accuracy, and low-pass filtering if necessary), or cascading multiple PID controllers.

3.1.7.1 Linearity Another problem faced with PID controllers is that they are linear, and in particular symmetric. Thus, performance of PID controllers in non-linear systems (such as HVAC systems) is variable. For example, in temperature control, a common use case is active heating (via a heating element) but passive cooling (heating off, but no cooling), so overshoot can only be corrected slowly – it cannot be forced downward. In this case the PID should be tuned to be over damped, to prevent or reduce overshoot, though this reduces performance (it increases settling time).

3.1.7.2 Noise in derivative A problem with the derivative term is that it amplifies higher frequency measurement or process noise that can cause large amounts of change in the output. It does this so much, that a physical controller cannot have a true derivative term, but only an approximation with limited bandwidth. It is often helpful to filter the measurements with a low-pass filter in order to remove higher-frequency noise components. As low-pass filtering and derivative control can cancel each other out, the amount of filtering is limited. So low noise instrumentation can be important. A nonlinear median filter may be used, which improves the filtering efficiency and practical performance. In some cases, the differential band can be turned off with little loss of control. This is equivalent to using the PID controller as a PI controller.


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Fuzzy Logic Controller

3.2.1 Introduction: In recent years the fuzzy logic has become popular in many applications of electrical drives and control, where classical PI controllers were previously used. Several design techniques exist to tune the classical PI controller parameters, but they can be time consuming and moreover fixed controller settings cannot usually provide good dynamic performance over the whole operating speed range of the drive. Varying load conditions, changes of mechanical parameters, process non linearity and inaccuracy in the process modeling can cause degradation of the performance. Fuzzy control technique does not need accurate system modeling. It employs the strategy adopted by the human operator to control complex process and gives superior performance than the conventional PI control. The fuzzy algorithm is based on human intuition and experience and can be regarded as a set of heuristic decision rules. It is possible to obtain very good performance in the presence of varying load conditions, changes of mechanical parameters and inaccuracy in the process modeling.

3.2.2 Proportional Integral Controller: To remove the steady state error in controlled variable of a process, an extra amount of intelligence must be added to the proportional controller. The extra intelligence is the integral (or) reset action. The PI controller produces a output signal consisting of two terms- one proportional to error signal and other proportional to the integral of error signal. The equation describing a PI controller is u (t)=Kc[e(t)+1/T1  e (t)dt]

…44

u(s)=Kc[1+1/T1(s)E(s)]

…45

Where T1 is integral (or) reset time

The integral (or) reset action in this controller removes the steady state error in the controlled variable. However, the integral mode of control has a considerable destabilizing effect, which in most of the situations, can be compensated by adjusting the gain (Kc).

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Fig 33: Control System Block Diagram of PI speed controller

Using the nominal drive model, the transfer function of rotor speed response to command input of can be expressed by At TL(s) =0

…46 Where,

…47 Owing to the absence of Zeros, the overshoot of the step response of back emf is avoided by setting the damping ratio ζ = 1. For convenience of designing the PI controller quantitatively, the response time is defined as the time required for the step response to rise from 0 to 90% of its final value.Solving the above nonlinear equation, we can obtain ωn , and then the value of PI controller parameters can be obtained. K1=(Jωn2)/Kt

,Kp=(2Jωn-B)/Kt

…48


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It follows from the above analysis that the desired tracking specifications can be completely achieved by using the simple PI controller.

3.2.3 Proposed Fuzzy Logic Control A matrix converter-based permanent magnet synchronous machine (PMSM) with fuzzy logic drive system to overcome the unflavored impact introduced by digital ﬁlter and guarantee the drive performance under input disturbance conditions. Transfer characteristics of matrix converter and output behaviors in terms of input voltage disturbances are analyzed.

3.2.3.1 Fuzzy Logic: Fuzzy logic (FL) is one of the artificial intelligent techniques. Fuzzy logic unlike Boolean logic, deals with problems that have fuzziness or vagueness. The classical set theory is based on Boolean logic, where particular object or variable is either a member of a given set (logic 1), or it is not (logic 0). On the other hand, in fuzzy set theory is based on fuzzy logic, where in a particular object has a degree of membership in a given set that may be anywhere in the range of 0 (completely not in the set) to 1 (completely in the set). For this reason, FL is often defined as multi-valued logic, compared to bi-valued Boolean logic. A FL problem can be defined as an input/output, static, nonlinear problem through a “black box”. All input information is defined in the input space, it is processed in the black box, and the solution appears in the output space. The mapping can be static or dynamic, and the mapping characteristic is determined by the black box’s characteristics. The black box need not necessary be a fuzzy system, but can also an Expert System (ES), neural network, general mathematical system such as differential equations, algebraic equations, etc., or anything else. FL processing is shown in

Fig 34: Input/Output Mapping Problem

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The first study about fuzzy set theory was developed by Zadeh. This theory permits to define control laws of any process starting from a linguistic description of the control strategy to be adopted. Fuzzy logic uses linguistic variables instead of numerical ones. A linguistic variable is a variable whose values (Fuzzy subsets) are labels or sentences in a natural or artificial language. A fuzzy subset A of a universe is represented by its characteristic or membership function µ AU − [0,1], which associates a number µA, called grade of membership , in the interval [0,1] to each element u of the universe. FLC is based on the collection control rules defined as fuzzy conditional statements of the type: if X is Ai and Y is Bi then Z is Ci Where X, Y, Z are linguistic variables and Ai, Bi and Ci are fuzzy subsets of X,Y,Z respectively.The antecedents of each rule are the inputs to the FLC while the consequents are the outputs.

3.2.3.2 Fuzzy System: A fuzzy inference system (or fuzzy system) basically consists of a formulation of the mapping from a given input set to an output set using FL. This mapping process provides the basis from which the inference or conclusion can be made. A fuzzy inference process consists of the following five steps. 

Fuzzification of input variables



Application of fuzzy operator (AND, OR, NOT) in the IF (antecedent part of the rule



Implication from the antecedent to the consequent (THEN part of the rule)



Aggregation of the consequents across the rules



Defuzzification

There are number of implication methods and defuzzification methods. Depending on the requirement choice is made on the implication and defuzzification methods. Three fundamental operations charactering a fuzzy system are 

Fuzzification of input crisp values



Fuzzy inference


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Defuzzification of fuzzy outpu

The operation of fuzzification converts the actual input values into linguistic values or fuzzy sets and gives the degree of fulfillment of the rules ‘antecedents’, i.e the degree to which each crisp input belongs to the corresponding fuzzy set. To fulfill all the antecedents of a rule these degrees have to be conjunctively aggregated by means of a T-norm operation (min, pro), which is performed among the membership degrees µ Ai(x0) and µ Bi(y0) of the antecedents corresponding to the current input values x0 and yo .The result is truth degree τi = T[µ Ai(x0) , µ Bi(y0)] of the antecedents of the ith rule. a membership function is a curve that defines how the values of a fuzzy variable in a certain region are mapped to a membership value µ (or degree of membership) between 0 and 1.fuzzy inference define as mapping from input fuzzy sets to output fuzzy sets based on the fuzzy IF-THEN rules and the compositional rule of inference and knowledge base contains information on fuzzy sets and a rule base with a set of linguistic conditional statements based on expert knowledge. With the given inputs, fuzzy inference computes the fuzzy set, having membership function µci(z)=min[µci(z),τi]

…49

induced by each rule and aggregates all output fuzzy sets to obtain the global fuzzy set, µout(z)=maxi{min[µci(z),τi]}

…50

Finally the output fuzzy set µ out(z) is converted into a crisp output value. This operation, called defuzzification, may be performed by several methods, like Mean-of-Maximum method, and Centre-of Gravity method (COG) . In COG method the output crisp value is performed by computing the zcoordinate of the centre of gravity of the area beneath the graph of the function

3.2.3.3 Fuzzy Implementation: It is a fuzzy set S with µ s (x,y) = µ A B (x,y) = µ A (x) *µ B (y) where A,B are fuzzy set on x,y respectively. When represents a minimum operator, it implies that the conclusion is no more certain than the premise. NormalizationKeeping all the universe of discourse fixed, the fuzzy system can be turned at its input and output with normalizing gains, making design easier and more flexible.

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A practical illustration of the operation of a fuzzy system is then given , for a Multiple- input single-output fuzzy system with 2 inputs e1 and e2, and 1 output, u. for each input or output, two fuzzy sets are shown, through usually there are more. e1, e2and u are numerical variables associated with linguistic values of the linguistic variables such as speed and torque, etc. ZE (zero), PS(positive small),and PL (positive large) are linguistic values of the linguistic variables. Given the values for e1 and e2 as shown as singleton fuzzification process maps them to associated fuzzy sets with membership values: e1 is mapped in to the fuzzy set representing "ZE" with the membership values of 0.75 and mapped in to the fuzzy set representing "PS" with the membership values of 0.25; e2 is mapped in to the fuzzy set representing "PS" with the membership values of 0.5. By using the sub-min inference method for both the premises and the fuzzy implication, as illustrated, and by using center of gravity defuzzification method for the shaded area, the desired output is then found

3.2.3.4 Fuzzy Control: The Control algorithm of process that is based on FL or fuzzy inference system is defined as a fuzzy control. In general, a control system based on Artificial Intelligent (AI) is defined as intelligent control. A fuzzy control essentially embeds the experience and intuition of a human plant operator, and sometimes those of a designer and/or researcher of a plant. The typical fuzzy control with the fuzzy system replacing a usual compensator in the loop. The knowledge base of the fuzzy system stores the expert knowledge on how to control the plant, while the inference engine stores the information how a human operator "in the loop" would use this knowledge to control the plant. The design of a conventional control system is normally based on the mathematical model of a plant. If an accurate mathematical model is available with known parameters it can be analyzed, for example, by a Bode plot or Nyquist plot, and a controller can be designed for the specified performance. Such a procedure is tedious and time-consuming, although CAD program are available for such design. Unfortunately, for complex processes, such as cement plants, nuclear reactors, and the like, a reasonably good mathematical model is difficult to find. On the other hand, the plant operator may have good experience for controlling Power electronics system models are ill defined. Even if a plant model is wellknown there may be parameter variation problems. Sometimes, the model is multivariable, complex, and non-linear, such as dynamic d-q model of an ac machine. Vector or field oriented control of a drive can overcome this problem,


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but accurate vector control is nearly impossible, and there may be wide parameter variation problem in the system. To combat such problems, various adaptive control techniques were used. On the other hand, fuzzy control is basically an adaptive and nonlinear control, which gives robust performance for a linear or nonlinear plant with parameter variation. FL applications in power electronics and motor drives are somewhat recent. Fuzzy adaptive, hybrid fuzzy controller gathered momentum to these power electronics and drives areas. From the above it is clear that, the advent of AI technology has brought new challenge to power electronics engineers who are struggling with complex, fast advancing technology.

3.2.4

FLC Structure

In the conventional control, the amount of control is determined in relation to a number of data inputs using a set of equations to express the entire control process. Expressing human experience in the form of a mathematical formula is a very difficult task, if not an impossible one. Fuzzy logic provides a simple tool to interpret this experience into reality. Fuzzy logic controllers are rule-based controllers. The structure of the FLC resembles that of a knowledge based controller except that the FLC utilizes the principles of the fuzzy set theory in its data representation and its logic. The basic configuration of the FLC can be simply represented in four parts,

Fig 35: FLC Structure • Fuzzification module – the functions of which are first, to read, measure, and scale the control variable (speed, acceleration) and, second, to transform the

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measured numerical values to the corresponding linguistic (fuzzy variables with appropriate membership values). • Knowledge base - this includes the definitions of the fuzzy membership functions defined for each control variables and the necessary rules that specify the control goals using linguistic variables. • Inference mechanism – it should be capable of simulating human decision making and influencing the control actions based on fuzzy logic; De fuzzification module which converts the inferred decision from the linguistic variables back The numerical values.

3.2.5

FLC Design

The design process of an FLC may split into the five steps described as: a. Selection of the control variables the selection of control variables (controlled inputs and outputs) depends on the nature of the controlled system and the desired output. Usually the output error (e) and the rate or derivative of the output (de) is used as controller inputs. Some researchers have also proposed the use of error and the integral of error as an input to the FLC. b. Membership function definition Each of the FLC input signal and output signal, fuzzy variables (Xj={e, de,u}), has the real line R as the universe of discourse. In practice, the universe of discourse is restricted to a comparatively small interval [Xminj, Xmaxj]. The universe of discourse of each fuzzy variables can be quantized into a number of overlapping fuzzy sets (linguistic variables). The number of fuzzy sets for each fuzzy variables varies according to the application. The reasonable number is an odd number (3,5,7…). Increasing the number of fuzzy sets results in a corresponding increase in the number of rules. Membership functions can be of a variety of shapes, the most usual being triangular, trapezoidal, singleton or an Exponential.

Fuzzy sets are defined for each input and output variable. There are seven fuzzy levels (LN - large negative, MN - medium negative, SN - small negative, Z zero, SP – small positive, MP - medium positive, LP - large positive) [7]. The membership functions for input and output variable are triangular. The min - max method inference engine is used; the defuzzify method used in this FLC is center of area. The complete set of control rules is shown in table.


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Table 5: Membership Table

Each of the 49 control rules represents the desired controller response to a particular situation. The block diagram presented in figure shows a FLC controller in the Matlab simulation (ANFIS edit) and in figure the simulation of the surface control is presented.

3.2.6

Fuzzy Inference Systems

A fuzzy inference system (FIS) is a system that uses fuzzy set theory to map inputs (features in the case of fuzzy classification) to outputs (classes in the case of fuzzy classification). Two FISs will be discussed here, the Mamdani and the Sugeno.

3.2.6.1 Fuzzy inference systems (Mamdani) To compute the output of this FIS given the inputs, one must go through six steps: 1. Determining a set of fuzzy rules 2. Fuzzifying the inputs using the input membership functions, 3. Combining the fuzzified inputs according to the fuzzy rules to establish a rule strength 4. Finding the consequence of the rule by combining the rule strength and the output membership function, 5. Combining the consequences to get an output distribution, and 6. Defuzzifying the output distribution (this step is only if a crisp output (class) is needed). The following is a more detailed description of this process.

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Fig 36: A two input, two rule Mamdani FIS with crisp inputs

3.2.6.2 Creating Fuzzy Rules Fuzzy rules are a collection of linguistic statements that describe how the FIS should make a decision regarding classifying an input or controlling an output. Fuzzy rules are always written in the following form: if(input1 is membership function1) and/or (input2 is membership function2) and/or. then (outputn is output membership functionn). For example, one could make up a rule that says: if temperature is high and humidity is high then room is hot. There would have to be membership functions that define what we mean by high temperature (input1), high humidity (input2) and a hot room (output1). This process of taking an input such as temperature and processing it through a membership function to determine what we mean by "high" temperature is called fuzzification. Also, we must define what we mean by "and" / "or" in the fuzzy rule. This is called fuzzy combination.


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3.2.6.3 Fuzzification The purpose of fuzzification is to map the inputs from a set of sensors (or features of those sensors such as amplitude or spectrum) to values from 0 to 1 using a set of input membership functions. their are two inputs, x 0 and y0 shown at the lower left corner. These inputs are mapped into fuzzy numbers by drawing a line up from the inputs to the input membership functions above and marking the intersection point. These input membership functions, as discussed previously, can represent fuzzy concepts such as "large" or "small", "old" or "young", "hot" or "cold", etc. For example, x0 could be the EMG energy coming from the front of the forearm and y0 could be the EMG energy coming from the back of the forearm. The membership functions could then represent "large" amounts of tension coming from a muscle or "small" amounts of tension. When choosing the input membership functions, the definition of what we mean by "large" and "small" may be different for each input.

3.2.6.4 Fuzzy combinations (T-norms) In making a fuzzy rule, we use the concept of "and", "or", and sometimes "not". The sections below describe the most common definitions of these "fuzzy combination" operators. Fuzzy combinations are also referred to as "T-norms". Fuzzy "and" The fuzzy "and" is written as:

where µA is read as "the membership in class A" and µB is read as "the membership in class B". There are many ways to compute "and". The two most common are: 1. Zadeh - min(uA(x), uB(x)) This technique, named after the inventor of fuzzy set theory simply computes the "and" by taking the minimum of the two (or more) membership values. This is the most common definition of the fuzzy "and". 2. Product - ua(x) times ub(x)) This techniques computes the fuzzy "and" by multiplying the two membership values. Both techniques have the following two properties: T(0,0) = T(a,0) = T(0,a) = 0

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T(a,1) = T(1,a) = a One of the nice things about both definitions is that they also can be used to compute the Boolean "and". shows the Boolean "and" operation. Notice that both fuzzy "and" definitions also work for these numbers. The fuzzy "and" is an extension of the Boolean "and" to numbers that are not just 0 or 1, but between 0 and 1. Table 6: The Boolean "AND" Input1 (A) Input2 (B) Output (A "and" B) 0

0

0

0

1

0

1

0

0

1

1

1

Fuzzy "or" The fuzzy "or" is written as: …51 Similar to the fuzzy "and", there are two techniques for computing the fuzzy "or": 1. Zadeh - max(uA(x), uB(x)) This technique computes the fuzzy "or" by taking the maximum of the two (or more) membership values. This is the most common method of computing the fuzzy "or". 2. Product - uA(x)+uB(x) - uA(x) uB(x) This technique uses the difference between the sum of the two (or more) membership values and the product of the membership values. Both techniques have the following properties: T(a,0) = T(0,a) = a T(a,1) = T(1,a) = 1


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Similar to the fuzzy "and", both definitions of the fuzzy "or" also can be used to compute the Boolean. The fuzzy "or" is an extension of the Boolean "or" to numbers that are not just 0 or 1, but between 0 and 1.

Table 7: The Boolean “OR” Input1 (A) Input2 (B) Output (A "or" B) 0

0

0

0

1

1

1

0

1

1

1

1

3.2.6.5 Consequence The consequence of a fuzzy rule is computed using two steps: 1. Computing the rule strength by combining the fuzzified inputs using the fuzzy combination. Notice in this example, the fuzzy "and" is used to combine the membership functions to compute the rule strength. 2. Clipping the output membership function at the rule strength.

3.2.6.6 Combining Outputs into an Output Distribution The outputs of all of the fuzzy rules must now be combined to obtain one fuzzy output distribution. This is usually, but not always, done by using the fuzzy "or". The output membership functions on the right hand side of the figure are combined using the fuzzy "or" to obtain the output distribution.

3.2.6.7 Defuzzification of Output Distribution In many instances, it is desired to come up with a single crisp output from a FIS. For example, if one was trying to classify a letter drawn by hand on a drawing

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tablet, ultimately the FIS would have to come up with a crisp number to tell the computer which letter was drawn. This crisp number is obtained in a process known as defuzzification. There are two common techniques for defuzzifying: 1. Center of mass - This technique takes the output distribution and finds its center of mass to come up with one crisp number. This is computed as follows:

…52 where z is the center of mass and uc is the membership in class c at value zj.

Fig 37: Defuzzification Using the Center of Mass

2. Mean of maximum - This technique takes the output distribution and finds its mean of maxima to come up with one crisp number. This is computed as follows:

where z is the mean of maximum, zjis the point at which the membership function is maximum, and l is the number of times the output distribution reaches the maximum level.


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Fig 38: Defuzzification using Mean of maximum

3.2.7

Fuzzy Inference System (Sugeno)

The Sugeno FIS is quite similar to the Mamdani FIS dThe primary difference is that the output consequence is not computed by clipping an output membership function at the rule strength. In fact, in the Sugeno FIS there is no output membership function at all. Instead the output is a crisp number computed by multiplying each input by a constant and then adding up the results. "Rule strength" in this example is referred to as "degree of applicability" and the output is referred to as the "action". Also notice that there is no output distribution, only a "resulting action" which is the mathematical combination of the rule strengths (degree of applicability) and the outputs

Fig 39: A two input, two rule Sugeno FIS (pn, qn, and rn) are user-defined constants

78 3.3

Control Strategies For Hybrid Renewable Energy Systems ANFIS CONTROLLER

This chapter presents the modeling and simulation of an adaptive neuro-fuzzy inference strategy (ANFIS) to control one of the most important parameters of the induction machine, viz., speed. IM’s are non-linear machines having a complex and time-varying dynamics. Some of the states are inaccessible during the operational stages and also many of the states are not available for measurements; hence, it is essential to design state observers. The control of IMs is thus a challenging problem to be taken up in the industries, especially in the control of speed in paper mills, etc. Various advanced control techniques have been devised by various researchers across the world, with some of them being based on hybrid techniques. Some of them have already been explained in the previous chapters. These fuzzy-based controllers develop a control signal that yields on the firing of the rule base, which is written on the previous experiences, which is random in nature. Thus, the outcome of the controller is also random and optimal results may not be obtained. Selection of the proper membership functions and in turn selecting the right rule base depending on the situation can be achieved by the use of an ANFIS controller, which becomes an integrated method of approach for control purposes and yields excellent results, which is the highlight of this chapter. In the designed ANFIS scheme, neural network techniques are used to select a proper rule base, which is achieved using the back-propagation algorithm. This integrated approach improves the designed controller’s performance in many ways in terms of cost-effectiveness and reliability. The simulation results presented at the end of this chapter prove that if the designed control is more effective, it has faster response times or settling times. The sudden fluctuation or change in speed and its effect on the various parameters of the dynamic system are also considered further in this chapter. The designed controller not only takes care of the sudden perturbations in speed, but also brings back the parameters to the reference or the set value in a few milliseconds, thus exhibiting the robustness in behavior. In other words, the designed controller is robust to parametric variations. A reasonable accuracy in the speed-control characteristics of the IM could be observed using this robust control scheme.


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Introduction

Artificial intelligence, ANN, Fuzzy Logic, hybrid networks, etc. have been recognized as main tools to improve the performance of power electronics-based drives in the industrial sectors. Currently, the combination of this intelligent control with adaptiveness appears as the most promising research area in the practical implementation and control of electrical drives. A review of the research carried out by various researchers regarding the ANFIS control of AC machines. The responses (speed) had taken a long time to reach the set value. In the research work presented in this chapter, an attempt is made to reduce the settling time of the responses (speed) and make the response very fast by designing an efficient controller using a hybrid type of ANFIS-based control strategy taking into account some of the parameters. An intelligently developed back-propagation algorithm could be used for NN training for the proper selection of the rule base. Here, we have formulated this complex control strategy for the speed control of IM, which yielded excellent result compared to the other methods mentioned in the literature survey.

3.3.2

ANFIS CONCEPT

ANN has strong learning capabilities at the numerical level. Fuzzy logic has a good capability of interpretability and can also integrate expert's knowledge. The hybridization of both paradigms yields the capabilities of learning, good interpretation and incorporating prior knowledge. ANN can be used to learn the membership values for fuzzy systems, to construct IF-THEN rules, or to construct decision logic. The true scheme of the two paradigms is a hybrid neural/fuzzy system, which captures the merits of both the systems. This concept is made use of in developing the ANFIS controller in this chapter. A neuro-fuzzy system has a neural-network architecture constructed from fuzzy reasoning. Structured knowledge is codified as fuzzy rules, while the adapting and learning capabilities of neural networks are retained. Expert knowledge can increase learning speed and estimation accuracy. Fuzzy logic is one of the most successful applications in the control engineering field, which can be used to control various parameters of real-time systems. This logic combined with neural networks yields very significant results. This merged technique of the learning power of the NNs with the knowledge representation of FL has created a new hybrid technique, called neuro-fuzzy networks. This technique gives a fairly good estimate of the speed and is robust to parameter

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variation.

3.3.2.1 The ANFIS Model The architecture of the ANFIS model is a graphical representation of the TS-FLC model. The general ANFIS control structure for the control of any plant is presented in this section. The functions of the various layers are given in the form of an algorithm as described below. The structure contains the same components as .fis, except for the NN block. The network structure is composed of a set of units arranged into 5 interconnected network layers Layer 1: This layer consists of input variables (membership functions), viz., input 1 and input 2. Here, triangular or bell-shaped MF can be used. This layer supplies the input values to the next layer, where i=1 to n. In other words, layer 1 is the input layer with n nodes. Layer 2: This layer (membership layer) checks the weights of each MF. It receives the input values xi from the 1st layer and acts as MFs to represent the fuzzy sets of the respective input variables. Furthermore, it computes the membership values that specify the degree to which the input value xi belongs to the fuzzy set, which acts as the inputs to the next layer. Note that layer 2 has nK nodes, each outputting the MF value of the ith antecedent of the jth rule given by yij(2)=μAji x(i), where A is a matrix defining a partition of the space xi and μAji(x(i)) is typically selected as a generalized bell MF defined by the equation: μAji(x(i))= μ(xi,cji,aji,bji). Note that the parameters cji,aji,bji in the above equation are referred to as the premise parameters. Layer 3: This layer performs the pre-condition matching of the fuzzy rules, i.e., they compute the activation level of each rule, the number of layers being equal to the number of fuzzy rules. Each node of these layers calculates the weights that are normalized. Layer 3 has K fuzzy neurons with the product t-norm as the aggregation operator. Each node corresponds to a rule and the output of the jth neuron determines the degree of the fulfillment of the jth rule given by for j=1,………, K. ) Layer 4: This layer provides the output values y, resulting from the inference of rules. Connections between the layers l3 and l4 are weighted by the fuzzy singletons that represent another set of parameters for the neuro-fuzzy network. Each neuron in layer 4 performs the normalization, and the outputs are called normalized firing strengths, which are given by


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…53 Layer 5: This layer is called the output layer, which sums up all the inputs coming from layer 4 and transforms the fuzzy classification results into a crisp (binary). The output of each node in the layer can be defined by for j=1,……,K, where is given for the jth node in layer 5. The outputs of layer 5 are summed up and the final output of the network can be re-written. The ANFIS structure is tuned automatically by least-square-estimation and the back-propagation algorithm. The algorithm shown above is used in the next section to develop the ANFIS controller to control the various parameters of the PWM Learning of the ANFIS Model: In the ANFIS model, functions used at all the nodes are differentiable; thus the BP algorithm can be used to train the network. Each MF μAij is specified by a pre-defined shape and its corresponding shape parameters. The shape parameters are adjusted by a BP learning algorithm using a sample set of size N (xt , yt). For non-linear modeling, the effectiveness of the model is dependent on the MFs used. The TSK fuzzy rules are employed in the ANFIS model as follows:

…54 where, Accordingly, the error measure at time t is defined by

…55 After the rule base is specified, the ANFIS adjusts only the MFs of the antecedents and the consequent parameters. The BP algorithm can be used to train both the premise and consequent parameters. A more efficient procedure is to learn the premise parameters by the BP, but to learn the linear consequent parameters ai,j by the RLS (Recursive Least Square) method. The learning rate η can be adaptively adjusted by using a heuristic approach.

82 3.3.3

Control Strategies For Hybrid Renewable Energy Systems ANFIS CONTROLLER DESIGN

In the modeling and feedback control of any dynamical system, a controller is a must for the plant as it takes care of all the disturbances and brings back the system to its original state in a couple of second.

Fig. 40: Block diagram of the ANFIS control scheme

To start the design of the controller using the ANFIS scheme, first, a mathematical model of the induction motor plant along with the controller mathematical model is required, which can be further used for simulation purposes. The mathematical model of the plant is given by Eq. (3.56) in Chapter 3 and it is a fourth-order mathematical model of size (4 4), which is used here in the Simulink model. The basic structure of the ANFIS coordination controller developed in this study to control the speed of the IM. The block diagram of the developed controller is shown in Fig. 7.1. Inputs to the ANFIS controller, i.e., the error and the change in error, are modeled using Eqs. (7.4) and (7.5) as follows: e(k) = ωref - ωr

…56

Δ e(k) = e(k) - e(k - 1)

…57

where ωref is the reference speed, ωr is the actual rotor speed, e(k) is the error and Δe(k) is the change in error.

The fuzzification unit converts the crisp data into linguistic variables, which is given as inputs to the rule-based block. The set of 49 rules is written on the basis of previous knowledge or experiences. The rule-based block is connected to the neural network block. Back-propagation algorithm is used for NN training in


83

order to select the proper set of rule base. For developing the control signal, training is a very important step in the selection of the proper rule base. Once the proper rules are selected and fired, the control signal required to obtain the optimal outputs is generated. The inputs are fuzzified using the fuzzy sets and are given as input to the ANFIS controller. The rule base for the selection of proper rules using the back-propagation algorithm is shown in Table 6.

Table 8: Rule base for controlling the PWM using ANFIS

The same rule base which was used for Mamdani-based FLC and TS-based FLC for decision-making purposes, is also used in this chapter for the decisionmaking purposes to design the ANFIS controller. The same 49 rules have been used here for control purposes and are not shown here for the sake of convenience. The control decisions are made based on the fuzzified variables in Table 8. The inference involves a set of rules for determining the output decisions. As there are 2 input variables and 7 fuzzified variables, the controller has a set of 49 rules for the ANFIS controller. Out of these 49 rules, the proper rules are selected by training the neural network with the help of back-propagation algorithm and

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these selected rules are fired. Furthermore, it has to be converted into numerical output. The output y is given as follows:

…58 This controlled output of the IM, i.e., y (here, it is the speed of the IM), is the weighted average of the proper rule-based outputs, which are selected by the back-propagation algorithm.

CHAPTER - 4

MATLAB SIMULTION OF SEPIC CONVERTER USING VARIOUS CONTROLLERS

4.1

INTRODUCTION:

Simulation has become a very powerful tool on the industry application as well as in academics, nowadays. It is now essential for an electrical engineer to understand the concept of simulation and learn its use in various applications. Simulation is one of the best ways to study the system or circuit behaviour without damaging it .The tools for doing the simulation in various fields are available in the market for engineering professionals. Many industries are spending a considerable amount of time and money in doing simulation before manufacturing their product. In most of the Research and Development (R&D) work, the simulation plays a very important role. Without simulation it is quiet impossible to proceed further. It should be noted that in power electronics, computer simulation and a proof of concept hardware prototype in the laboratory are complimentary to each other. However computer simulation must not be considered as a substitute for hardware prototype. The objective of this chapter is to describe simulation of impedance source inverter with R, R-L and RLE loads using MATLAB tool.

4.2

INTRODUCTION TO MATLAB

MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses includes 1. Math and computation 2. Algorithm development 3. Data acquisition

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Control Strategies For Hybrid Renewable Energy Systems 4. Modeling, simulation, and prototyping 5. Data analysis, exploration, and visualization 6. Scientific and engineering graphics 7. Application development, including graphical user interface building

MATLAB is an interactive system whose basic data element is an array that does not require dimensioning. This allows you to solve many technical computing problems, Especially those with matrix and vector formulations, in a fraction of the time it would take to write a program in a scalar non interactive language such as C. The name MATLAB stands for matrix laboratory. MATLAB was originally written to provide easy access to matrix software developed by the LINPACK and EISPACK projects. Today, MATLAB engines incorporate the LAPACK and BLAS libraries, embedding the state of the art in software for matrix computation. MATLAB has evolved over a period of years with input from many users. In university environments, it is the standard instructional tool for introductory and advanced courses in mathematics, engineering, and science. In industry, MATLAB is the tool of choice for high-productivity research, development, and analysis. MATLAB features a family of add-on application-specific solutions called toolboxes. Very important to most users of MATLAB, toolboxes allow you to learn and apply specialized technology. Toolboxes are comprehensive collections of MATLAB functions (M-files) that extend the MATLAB environment to solve particular classes of problems. Areas in which toolboxes are available include signal processing, control systems, neural networks, fuzzy logic, wavelets, simulation, and many others.

4.3

MATLAB SYSTEM

The MATLAB system consists of main parts, they are:

4.3.1

Desktop Tools and Development Environment

This is the set of tools and facilities that help you use MATLAB functions and files. Many of these tools are graphical user interfaces. It includes the MATLAB


87

desktop and Command Window, a command history, an editor and debugger, a code analyser and other reports, and browsers for viewing help, the workspace, files, and the search path.

4.3.2

The MATLAB Mathematical Function Library

This is a vast collection of computational algorithms ranging from elementary functions, like sum, sine, cosine, and complex arithmetic, to more sophisticated functions like matrix inverse, matrix Eigen values, Bessel functions, and fast Fourier transforms.

4.3.3

The MATLAB Language

This is a high-level matrix/array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both "programming in the small" to rapidly create quick and dirty throw-away programs, and "programming in the large" to create large and complex application programs.

4.3.4

Graphics

MATLAB has extensive facilities for displaying vectors and matrices as graphs, as well as annotating and printing these graphs. It includes high-level functions for two-dimensional and three-dimensional data visualization, image processing, animation, and presentation graphics. It also includes low-level functions that allow you to fully customize the appearance of graphics as well as to build complete graphical user interfaces on your MATLAB.

4.3.5

MATLAB External Interfaces/API

This is a library that allows you to write C programs that interact with MATLAB. It includes facilities for calling routines from MATLAB (dynamic linking), calling MATLAB as a computational engine.

4.3.6

MATLAB Documentation

MATLAB provides extensive documentation, in both printed and online format, to help you learn about and use all of its features. If you are a new user, start with

88


this Getting Started book. It covers all the primary MATLAB features at a high level, including many examples. The MATLAB online help provides taskoriented and reference information about MATLAB features.

4.3.7

MATLAB Online Help

To view the online documentation, select MATLAB Help from the Help menu in MATLAB. The Role of Simulation in Design: Electrical power systems are combinations of electrical circuits and electro mechanical devices like motors and generators. Engineers working in this discipline are constantly improving the performance of the systems. Requirements for drastically increased efficiency have forced power system designers to use power electronic devices and sophisticated control system concepts that tax traditional analysis tools and techniques. Further complicating the analyst's role is the fact that the system is often so nonlinear that the only way to understand it is through simulation. Land-based power generation from hydroelectric, steam, or other devices is not the only use of power systems. A common attribute of these systems is their use of power electronics and control systems to achieve their performance objectives. Sim Power Systems is a modern design tool that allows scientists and engineers to rapidly and easily build models that simulate power systems. Sim Power Systems uses the Simulink environment, allowing you to build a model using simple click and drag procedures. Not only can you draw the circuit topology rapidly, but your analysis of the circuit can include its interactions with mechanical, thermal, control, and other disciplines. This is possible because all the electrical parts of the simulation interact with the extensive Simulink modelling library. Since Simulink uses MATLAB as its computational engine, designers can also use MATLAB toolboxes and Simulink block sets. Sim Power Systems and Sim Mechanics share a special Physical Modeling block and connection line interface.

4.4

Sim Power Systems libraries

You can rapidly put Sim Power Systems to work. The libraries contain models of typical power equipment such as transformers, lines, machines, and power electronics. These models are proven ones coming from textbooks, and their validity is based on the experience of the Power Systems Testing.


89

Fig 41: Matlab Library

American utility located in Canada, and also on the experience of Ecole de Technologies superior and University Laval. The capabilities of Sim Power Systems for modelling a typical electrical system are illustrated in demonstration files. And for users who want to refresh their knowledge of power system theory, there are also self-learning case studies. The Sim Power Systems main library, powerlib, organizes its blocks into libraries according to their behaviour. The powerlib library window displays the block library icons and names. Double-click a library icon to open the library and access the blocks. The main Sim Power Systems powerlib library window also contains the powergui block that opens a graphical user interface for the steadystate analysis of electrical circuits.

4.5

SIMULATION RESULTS

We have discussed individual explanations of each components used in this project. Let us see the simulation results

4.5.1

Control of SEPIC converter using PID controller

Below shown is the simulink diagram of PID based PWM control of SEPIC converter

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Fig 42: Overall diagram of PID controlled SEPIC

4.5.1.1 Solar Array

Fig 43: Solar Array subsystem


Fig 44: Solar array Voltage

Fig 45: Solar array Current

91

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4.5.1.2 Wind energy

Fig 46: Wind energy subsystem

Fig 47: Wind turbine Voltage

Control Strategies For Hybrid Renewable Energy Systems 4.5.1.3 SEPIC converter

Fig 48: SEPIC subsystem

4.5.1.4 MPPT subsystem

Fig 49: MPPT subsystem

93

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Fig 50: Embedded MPPT subsystem

4.5.1.5 PID controller

Fig 51: PID block parameters


95

4.5.1.6 PWM control

Fig 52: PWM control subsystem

Fig 53: Pulse and sawtooth waveform

4.5.2

Control of SEPIC converter using Fuzzy logic controller

Below shown is the simulink diagram of Fuzzy Logic based PWM control of SEPIC converter

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Fig 54: Overall diagram of Fuzzy Logic controlled SEPIC

Starting from solar array, wind system, MPPT and PWM control has same Figures as shown. Now let’s see Fuzzy Logic Controller block.

4.5.2.1 Fuzzy Logic Controller

Fig 55: Fuzzy Logic subsystem


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Fig 56: Fuzzy rule base

4.5.3

Control of SEPIC converter using ANFIS controller

Below shown is the simulink diagram of ANFIS based PWM control of SEPIC converter

Fig 57: Overall diagram of ANFIS controlled SEPIC

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Starting from solar array, wind system, MPPT and PWM control has same Figures as shown. Now let’s see ANFIS Controller block.

4.5.3.1 ANFIS Controller

Fig 58: ANFIS subsystem

Fig 59: ANFIS editor


Fig 60: ANFIS rule editor

Fig 61: ANFIS structure

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100 4.5.4

Control Strategies For Hybrid Renewable Energy Systems Simulation parameters and values Table 9: Simulation parameters and its values SIMULATION PARAMETERS

VALUES

SOLAR ARRAY PARAMETERS Generation voltage

15 V

Resistance

0.0001 Ohms

Capacitance

2.2 micro Farad

WIND TURBINE PARAMETERS Generation voltage

15V

Capacitance

2.2 micro Farad

DIODE RECTIFIER Resistance

0.0001 Ohms

Forward voltage

0.8 V

Snubber resistance

500 Ohms

Snubber Capacitance

250 nano Farad

SEPIC CONVERTER Inductance

3 milli Henry

Capacitance

2.22 nano Farad

MOSFET (SEPIC) Resistance

0.1 Ohms

Internal diode Resistance

0.01 Ohms

Snubber Resistance

1*105 Ohms

Snubber Capacitance

infinity

THREE PHASE INVERTER Snubber Resistance

100 Kilo Ohms

Snubber Capacitance

1 micro Farad

IGBT Resistance

1 micro Ohms

IGBT Forward Voltage

1.1 V

Diode Forward Voltage

0.75 V

Fall time

1 microsecond

Tail time

2 microseconds


101

LOAD Resistance

100 Ohms

LP SECOND ORDER FILTER Cut off frequency

500 Hz

Damping factor

0.707

PID CONTROLLER (CONTINUOUS TIME)

4.5.5

Proportional

1

Integral

5

Derivative

1

Filter coefficient

100

Output / Result and comparative analysis of simulink files

We have seen simulink pictures of SEPIC converter using various controllers. Now we can see the results concerning to various controllers.

4.5.5.1 DC Output from SEPIC converter

Fig 62: DC Output of PID controlled SEPIC converter

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Fig 63: DC Output of Fuzzy Logic controlled SEPIC converter

Fig 64: DC Output of ANFIS controlled SEPIC converter

On comparison of all 3 DC outputs from SEPIC converter it is seen that ANFIS control of DC voltage is seen within limits without much disturbance and for same input ANFIS provides boost voltage of nearly 200 V which is a major advantage of using this control technique. This ANFIS when used to control SEPIC converter in HVDC converter station can provide stabilized power to the DC grid.


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Now we will see the three phase outputs for all three control techniques at resistance load end.

Fig 65: 3 phase output of PID controlled SEPIC converter

Fig 66: 3 phase output of Fuzzy Logic controlled SEPIC converter

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Fig 67: 3 phase output of ANFIS controlled SEPIC converter

From the above results it is seen that due to stable conversion of DC voltage, 3 phase output is nearly sinusoidal. On the other hand, 3 phase output of PID and Fuzzy controller has harmonics and frequency disturbances.

Table 10: DC and AC outputs for ANFIS based SEPIC HYBRID DC INPUT

SEPIC OUTPUT

BOOST AC OUTPUT FOR 3 PHASE RESISTIVE LOAD Req = 33.3 OHMS

8 - 10 V

150 – 170 V

150 – 160 V

13 - 15 V

200 – 210 V

200 – 220 V

18 – 20 V

240 – 260 V

240 – 260 V

Above table gives the corresponding AC and DC outputs of proposed ANFIS based SEPIC system. By varying AC load, these results may vary. But by changing the boost factor of ANFIS we could control the DC or AC output.

CHAPTER - 5

HARDWARE MODEL

5.1 INTRODUCTION

SINGLE PHASE AC SOURCE WITH RECTIFIER PV CELL

SEPIC CONVERT ER

DC OUTPUT

PIC (BATTERY CONTROL WITH LER FOR FORWARD PWM BIAS GENERATI Fig 68: Hardware implementation of SEPIC converter. DIODE) ON DRIVER CIRCUIT

For the hardware implementation we use different components. They are listed below as 1. SEPIC Converter. 2. PWM pulse generator. 3. Driver circuit..

106 5.2

Control Strategies For Hybrid Renewable Energy Systems SEPIC CONVERTER

Fig 69: SEPIC onboard implementation

SEPIC Converter consists of following components: 1. MOSFET IRF840 2. 1 uH INDUCTOR 3. 1500 uF 100 V CAPACITOR 4. 5.2.1

MOSFET IRF840

5.2.1.1 MOSFET INTRODUCTION The following sections describe the components used and their properties affecting the design of multi level inverter. The MOSFET or Metal Oxide Semiconductor Field Effect Transistors by the far most common field effect transistor in both digital and analog circuits. The MOSFET is composed of a channel of n-type or p-type semiconductor material, and is accordingly called as N-MOSFET or a P-MOSFET. Unfortunately, many semiconductors with better electrical properties than silicon such as gallium arsenide do not form good gate oxides and thus are not suitable for MOSFETs.


107

The gate terminal is a layer of poly silicon (polycrystalline silicon) or aluminums placed over a channel, but separated from the channel by a thin layer of insulating silicon dioxide. A simplified diagram of the N-channel enhancement MOSFETS is shown in figure. Drain and source connections are made to higher conduction high doped regions. The metal gate is electrically isolated from the P-type substrate by a layer of non-conducting silicon oxide (SiO2). When a positive voltage is applied to the gate with respect to the source an electric field will be created pointing away from the base and across the P-region directly under the base. The electric field will cause positive charges in the P-region to move away from the base inducing or enhancing an N-region in its place. Conduction can then take place between the N+ (drain) N (enhanced region) N+(source) . Increasing or decreasing in size thus controlling conduction. Varying the voltage between the gate and body modulates the conductivity of this layer and makes it possible to control the current flow between drain and source.

Fig 70: Simple model of an N-channel enhancement type MOSFET

In practice, a fairly large current in the order of 1-2A can be required to charge the gate capacitance at turn ON to ensure that switching times are small. Due to gate leakage current, nano-amps are needed to maintain the gate voltage once the device is ON.

108


A negative voltage is often applied at turn OFF to discharge the gate for speedy switch OFF. It is obvious that faster switching speeds can be obtained with well designed gate driver circuits.

5.2.1.3 FEATURES OF POWER MOSFETS Power MOSFET has lower switching losses but its on-resistance and conduction losses are more. MOSFET is a voltage-controlled device. MOSFET has positive temperature co-efficient for resistance. This makes parallel operation of MOSFET easy. If a MOSFET shares increased current initially, it heats up faster its resistance rises and this increased resistance causes this current to shift to other devices in parallel. In MOSFET secondary break down does not occur, because it has positive temperature co-efficient. Power MOSFETs in higher voltage ratings have more conduction losses

5.2.1.4 IRF 840- POWER MOSFET 1) Dynamic dv/dt Rating 2) Repetitive Avalanche Rated 3) Fast switching 4) Ease of paralleling 5) Simple Drive requirements.

5.2.1.5 DESCRIPTION The IRF-840 provides fast switching, ruggedized device design, low onresistance and cost effectiveness. The TO-220 package is universally preferred for all commercial-industrial applications at power dissipation levels to approximately 50 watts. The low thermal resistance and low package cost of the TO-220 contribute to its wide acceptance throughout the industry. This N-Channel enhancement mode silicon gate power field effect transistor is an advanced power MOSFET designed, tested, and guaranteed to withstand a specified level of energy in the breakdown avalanche mode of operation. All of


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these power MOSFETs are designed for applications such as switching regulators, switching converters, motor drivers, relay drivers, and drivers for high power bipolar switching transistors requiring high speed and low gate drive power. These types can be operated directly from integrated circuits.

5.2.2

INDUCTOR

An inductor is a passive electronic component that stores energy in the form of a magnetic field. In its simplest form, an inductor consists of a wire loop or coil. The inductance is directly proportional to the number of turns in the coil. Inductance also depends on the radius of the coil and on the type of material around which the coil is wound. For a given coil radius and number of turns, air cores result in the least inductance. Materials such as wood, glass, and plastic - known as dielectric materials - are essentially the same as air for the purposes of inductor winding. Ferromagnetic substances such as iron, laminated iron, and powdered iron increase the inductance obtainable with a coil having a given number of turns. In some cases, this increase is on the order of thousands of times. The shape of the core is also significant. Toroidal (donut-shaped) cores provide more inductance, for a given core material and number of turns, than solenoidal (rod-shaped) cores. The standard unit of inductance is the henry, abbreviated H. This is a large unit. More common units are the microhenry, abbreviated µH (1 µH =10-6H) and the millihenry, abbreviated mH (1 mH =10-3 H). Occasionally, the nanohenry (nH) is used (1 nH = 10-9 H). It is difficult to fabricate inductors onto integrated circuit (IC) chips. Fortunately, resistors can be substituted for inductors in most microcircuit applications. In some cases, inductance can be simulated by simple electronic circuits using transistors, resistors, and capacitors fabricated onto IC chips. Inductors are used with capacitors in various wireless communications applications. An inductor connected in series or parallel with a capacitor can provide discrimination against unwanted signals. Large inductors are used in the power supplies of electronic equipment of all types, including computers and their peripherals. In these systems, the inductors help to smooth out the rectified utility AC, providing pure, battery-like DC.

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5.2.3 CAPACITOR Capacitor is a passive element that stores electric charge statistically and temporarily as a static electric field. It is composed of two parallel conducting plates separated by non-conducting region that is called dielectric, such as vacuum, ceramic, air, aluminum, etc. The capacitance formula of the capacitor is represented by, C is the capacitance that is proportional to the area of the two conducting plates (A) and proportional with the permittivity ε of the dielectric medium. The capacitance decreases with the distance between plates (d). We get the greatest capacitance with a large area of plates separated by a small distance and located in a high permittivity material. The standard unit of capacitance is Farad, most commonly it can be found in micro-farads, picofarads and nano-farads. General uses of Capacitors 1. Smoothing, especially in power supply applications which required converting the signal from AC to DC. 2. Storing Energy. 3. Signal decoupling and coupling as a capacitor coupling that blocks DC current and allow AC current to pass in circuits. 4. Tuning, as in radio systems by connecting them to LC oscillator and for tuning to the desired frequency. 5. Timing, due to the fixed charging and discharging time of capacitors. 6. For electrical power factor correction and many more applications.

5.2.3.1 CHARGING A CAPACITOR Capacitors are mainly categorized on the basis of dielectric used in them. During choosing a specific type of capacitors for a specific application, there are numbers of factors that get considered. The value of capacitance is one of the vital factors to be considered. Not only this, many other factors like, operating voltage, allowable tolerance stability, leakage resistance, size and prices are also very important factors to be considered during choosing specific type of capacitors.


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We know that capacitance of a capacitor is given by, Hence, it is cleared that, by varying ε, A or d we can easily change the value of C. If we require higher value of capacitance (C) we have to increase the cross-sectional area of dielectric or we have to reduce the distance of separation or we have to use dielectric material with stronger permittivity. If we go only for the increasing area of cross-section, the rise of the capacitor may become quite large; which may not be practically acceptable. Again if we reduce only the distance of separation, the thickness of dielectric becomes very thin. But the dielectric cannot be made too thin in case its dielectric strength in exceeded.

5.2.3.2 TYPES OF CAPACITORS The various types of capacitors have been developed to overcome these problems in a number of ways.

5.2.3.2.1 PAPER CAPACITOR It is one of the simple forms of capacitors. Here, a waxed paper is sandwiched between two aluminium foils. Process of making this capacitor is quite simple. Take place of aluminium foil. Cover this foil with a waxed paper. Now, cover this waxed paper with another aluminium foil. Then roll up this whole thing as a cylinder. Put two metal caps at both ends of roll. This whole assembly is then encapsulated in a case. By rolling up, we make quite a large cross-sectional area of capacitor assembled in a reasonably smaller space.

Fig 71: Paper capacitor

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5.2.3.2.2 AIR CAPACITOR There are two sets of parallel plates. One set of plates is fixed and another set of plates is movable. When the knob connected with the capacitor is rotated, the movable set of plates rotates and overlapping area as between fixed and movable plates vary. This causes variation in effective cross-sectional areas of the capacitor. Consequently, the capacitance varies when one rotates the knob attached to the air capacitor. This type of capacitor is generally used to tune the bandwidth of a radio receiver.

Fig 72: Air capacitor

5.2.3.2.3 PLASTIC CAPACITOR When various plastic materials are used as dielectric material, the capacitors are said to be plastic capacitors. The plastic material may be of polyester, polystyrene, polycarbonate or poly propylene. Each of these materials has slightly different electrical characteristics, which can be used to advantage, depending upon the proposed application. This type of capacitors is constructional, more or less same as paper capacitor. That means, a thin sheet one of the earlier mentioned plastic dielectrics, is kept between two aluminium foils. That means, here the flexible thin plastic sheet is used as dielectric instead of waxed paper. Here, the plastic sheet covered by aluminium foil from two sides, is first rolled up, then fitted with metal end caps, and then the whole assembly is encapsulated in a case.


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Fig 73: Plastic capacitor

5.2.3.2.4 SILVERED MICA CAPACITOR A silvered mica capacitor is very accurate and reliable capacitor. This type of capacitors has very low tolerance. But on the other hand, cost of this capacitor is quite higher compared to other available capacitors in the market. But this high cost capacitor can easily be compensated by its high quality and performance. A small ceramic disc or cylinder is coated by silver compound. Here, electrical terminal is affixed on the silver coating and the whole assembly is encapsulated in a casing.

5.2.3.2.5 CERAMIC CAPACITOR Construction of ceramic capacitor is quite simple. Here, one thin ceramic disc is placed between two metal discs and terminals are soldered to the metal discs. Whole assembly is coated with insulated protection coating as shown in the figure below.

Fig 74: Ceramic capacitor

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5.2.3.2.6 ELECTROLYTE CAPACITOR Very large value of capacitance can be achieved by this type of capacitor. But working voltage level of this electrolyte capacitor is low and it also suffers from high leakage current. The main disadvantage of this capacitor is that, due to the use of electrolyte, the capacitor is polarized. The polarities are marked against the terminals with + and – sign and the capacitor must be connected to the circuit in proper polarity. A few micro meter thick aluminium oxide or tantalum oxide film is used as dielectric of electrolyte capacitor. As this dielectric is so thin, the capacitance of this type of capacitor is very high. This is because; the capacitance is inversely proportional to thickness of the dielectric. Thin dielectric obviously increases the capacitance value but at the same time, it reduces working voltage of the device. Tantalum type capacitors are usually much smaller in size than the aluminium type capacitors of same capacitance value. That is why, for very high value of capacitance, aluminium type electrolyte capacitors do not get used generally. In that case, tantalum type electrolyte capacitors get used. Aluminium electrolyte capacitor is formed by a paper impregnated with an electrolyte and two sheets of aluminium. These two sheets of aluminium are separated by the paper impregnated with electrolyte. The whole assembly is then rolled up in a cylindrical form, just like a simple paper capacitor. This roll is then placed inside a hermetically sealed aluminium canister. The oxide layer is formed by passing a charging current through the device, and it is the polarity of this charging process that determines the resulting terminal polarity that must be subsequently observed. If the opposite polarity is applied to the capacitor, the oxide layer is destroyed. Table 11: Dielectric Properties of Various Materials Material

Dielectric constant

Dielectric Strength Volts/.001 inch

Air

1

80

Paper(Oiled)

3-4

1500

Mica

4-8

1800

Glass

4-8

200

Porcelain

5

750

Titanates

100-200

100


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PWM GENERATOR

Fig 75: PWM generator onboard implementation

PWM generator consists of following major components: 3. Power circuit for microprocessor. 4. PIC 16F877A 5. Crystal oscillator. 6. LED.

5.3.1

POWER CIRCUIT FOR MICROPROCESSOR U1 1

VIN

VOUT

3

7805

D1 1

R1

2

3

C1 47uF

C1 0.1uF

D2 LN211WP

-

TX1

JP1

330 C1 470uF

1

+

W01G

2

230V

4

V1

230/9V

Fig 76: Power circuit for Microprocessor

2 1 2 1

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Major components of power supply units are 

Step down Transformer



Rectifier diodes



Filters

5.3.1.1 DC POWER SUPPLY CIRCUIT As we all know any invention of latest technology cannot be activated without the source of power. So it this fast moving world we deliberately need a proper power source which will be apt for a particular requirement. All the electronic components starting from diode to Intel IC’s only work with a DC supply ranging from +5v to +12. We are utilizing for the same, the cheapest and commonly available energy source of 230v-50Hz.

5.3.1.2 STEP DOWN TRANSFORMER When AC is applied to the primary winding of the power transformer it can be stepped down or up depending on the value of DC needed. In our circuit the transformer of 230v/15-0-15v is used to perform the step down operations where a 230V AC appears as 15V across the secondary winding. One alteration of input causes the top of the transformer to be positive and the bottom negative. The next alteration will temporarily cause the reverse. The current rating of the transformer used in our project is 2A. Apart from stepping down AC voltages, it gives isolation between the power source and power supply circuitries.

5.3.1.3 RECTIFIER UNIT In the power supply unit, rectification is normally achieved using a solid-state diode. Diode has the property that will let the electron flow easily in one direction at proper biasing condition. As AC is applied to the diode, electrons only; flow when the anode and cathode is negative. Reversing the polarity of voltage will not permit electron flow. A commonly used circuit for supplying large amounts of DC power in the bridge rectifier. A bridge rectifier of four diodes (4*IN4007) is used to achieve full wave rectification. Two diodes will conduct during the positive half cycle. The DC voltage appearing across the output terminals of the bridge rectifier will be a


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somewhat lasso than 90% of the applied rms value. Normally one alteration of the input voltage will reverse the polarities. Opposite ends of the transformer will therefore always be 180 deg out of phase with each other.

5.3.1.4 FILTERING UNIT Filter circuits, which usually capacitor is acting as a surge arrester always follow the rectifier unit. This capacitor is also called as a decoupling capacitor or a bypassing capacitor, is used not only to ‘short’ the ripple with frequency of 120Hz to ground but also to leave the frequency of the DC to appear at the output. A load resistor R1 is connected so that a reference to the ground is maintained. C1R1 is for bypassing ripples. C2R2 is used as a low pass filter, i.e. it passes only low frequency signals and bypasses high frequency signals. The load resistor should be 

1% to 2.5% of the load.



1000µf/25v: for the reduction of ripples from the pulsating.



10µf/25v: for maintaining the stability of the voltage at the load side.



1µf: for bypassing the high frequency disturbances.

5.3.1.5 VOLTAGE REGULATOR The voltage regulators play an important role in any power supply unit. The primary purpose of a regulator is to aid the rectifier and filter circuit in providing a constant DC voltage to the device. Power supplies without regulators have an inherent problem of changing DC voltage values due to variations in the load or due to fluctuations in the AC liner voltage. With a regulator connected to the DC output, the voltage can be maintained within a close tolerant region of the desired output.

Fig 77: IC 7805

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Pin out of the 7805 regulator IC. 

1. Unregulated voltage in.



2. Ground.



3. Regulated voltage out.

Few significant points regarding the power circuit: 

A step-down transformer (230/15) V is used to give input supply to the power circuit.



The 15V AC input is rectified into 15V pulsating DC with the help of full bridge rectifier circuit.



The ripples in the pulsating DC are removed and pure DC is obtained by using a capacitor filter.



An output voltage of 5V obtained from the output pin of 7805 is fed as the supply to the micro controller.



From the same output pin of the 7805, a LED is connected in series with the resistor to indicate that the power is ON.

5.3.2

PIC CONTROLLER

In this project the hardware is implemented using the PIC Microcontroller “PIC 16F877A”. The advantages of the PIC- microcontroller is that the instruction set of this controller are fewer than the usual microcontroller. Unlike Conventional processors, which are generally complex, instruction set computer (CISC) type, PIC microcontroller is a RISC processor. The advantages of RISC processor against CISC processor are: 1. RISC instructions are simpler and consequently operate faster. 2. A RISC processor takes a single cycle for each instruction, while CISC processor requires multiple clocks per instruction ( typically, at least three cycles of throughput execution time for the simplest instruction and 12 to 24 clock cycles for more complex instruction), which makes decoding a tough task. 3. The control unit in a CISC is always implemented by a micro-code, which is much slower than the hardware implemented in RISC.


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The idea of using the PIC microcontroller is because: 1. To employ the frequently used instructions as the instruction set while using a few instructions to achieve the same function performed by a much more complex instruction in a CISC. 2. The RISC itself has a large number of general purpose registers, largely reduced the frequency of the most time-consuming memory access. 3. In terms of clock rate, the RISC with its much simpler circuits can have a higher clock rate that again increases the performance of a processor. Overall the RISC processor can provide processing power more than three times of a CISC processor in a particular field of application.

5.3.2.1 FEATURES OF PIC-MICROCONTROLLER “PIC16F877A” 1. Only 35 single word instructions to learn. 2. All instructions single-cycle except for program branches which are two - cycle. 3. Operating speed: DC - 20 MHz clock input DC - 200 ns instruction cycle 4. 1024 words of program memory 5. 368 bytes of Data RAM 6. 256 bytes of Data EEPROM 7. 14-bit wide instruction words 8. 8-bit wide data bytes 9. 15 Special Function Hardware registers 10. Eight-level deep hardware stack 11. Direct, indirect and relative addressing modes 12. Four interrupt sources: - External RB0/INT pin - TMR0 timer overflow - PORTB interrupt-on-change - Data EEPROM write complete

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5.3.2.2 BLOCK DIAGRAM

Fig 78: Block Diagram of PIC16F877A


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5.3.2.3 PIN DIAGRAM

Fig 79: Pin diagram of PIC16F877A

The PIC16F877A belongs to the mid-range family of the PIC microcontroller devices. A block diagram of the device is shown in Figure.78. The program memory contains 1K words, which translates to 1024 instructions, since each 14bit program memory word is the same width as each device instruction. The data memory (RAM) contains 368 bytes. Data EEPROM is 256 bytes. There are also 13 I/O pins that are user-configured on a pin-to-pin basis. Some pins are multiplexed with other device functions. These functions include: 1. External interrupt 2. Change on PORTB interrupts 3. Timer0 clock input

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5.3.2.4 MEMORY ORGANIZATION There are two memory blocks in the PIC16F877A. These are the program memory and the data memory. Each block has its own bus, so that access to each block can occur during the same oscillator cycle. The data memory can further be broken down into the general purpose RAM and the Special Function Registers (SFRs). The operations of the SFRs that control the “core” are described here. The SFRs used to control the peripheral modules are described in the section discussing each individual peripheral module. The data memory area also contains the data EEPROM memory. This memory is not directly mapped into the data memory, but is indirectly mapped. That is, an indirect address pointer specifies the address of the data EEPROM memory to read/write. The 256 bytes of data EEPROM memory have the address range 0h3Fh.

5.3.2.5 DATA EEPROM MEMORY The EEPROM data memory is readable and writable during normal operation (full VDD range). This memory is not directly mapped in the register file space. Instead it is indirectly addressed through the Special Function Registers. There are four SFRs used to read and write this memory. These registers are: 1. EECON1 2. EECON2 (not a physically implemented register) 3. EEDATA 4. EEADR EEDATA holds the 8-bit data for read/write, and EEADR holds the address of the EEPROM location being accessed. PIC16F877A devices have 256 bytes of data EEPROM with an address range from 0h to 3Fh. The EEPROM data memory allows bytes read and write. A byte write automatically erases the location and writes the new data (erase before write). The EEPROM data memory is rated for high erase/write cycles. The write time is controlled by an on-chip timer. The write time will vary with voltage and temperature as well as from chip to chip. Please refer to AC specifications for exact limits. When the device is code protected, the CPU may continue to read and write the data EEPROM memory. The device programmer can no longer access this memory.


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5.3.2.6 I/O PORTS Some pins for these I/O ports are multiplexed with an alternate function for the peripheral features on the device. In general, when a peripheral is enabled, that pin may not be used as a general purpose I/O pin.

5.3.2.7 TIMER 0 MODULE The Timer 0 module timer/counter has the following features: 1. 8-bit timer/counter. 2. Readable and writable. 3. Internal or external clock select. 4. Edge select for external clock. 5. 8-bit software programmable pre-scaler. 6. Interrupt-on-overflow from FFh to 00h.

5.3.2.8 SPECIAL FEATURES OF PIC16F877A What set a microcontroller apart from other processors are special circuits to deal with the needs of real time applications. The PIC16F877A has a host of such features intended to maximize system reliability, minimize cost through elimination of external components, provide power saving operating modes and offer code protection. These features are: 1. OSC Selection 2.

RESET

3. Interrupts 4. Watchdog Timer (WDT) 5. SLEEP 6. Code Protection 7. ID Locations 8. In-Circuit Serial Programming (ICSP)

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The PIC16F877A has a Watchdog Timer which can be shut-off only through configuration bits. It runs off its own RC oscillator for added reliability. There are two timers that offer necessary delays on power-up. One is the Oscillator Start-up Timer (OST), intended to keep the chip in RESET until the crystal oscillator is stable. The other is the Power-up Timer (PWRT), which provides a fixed delay of 72 ms (nominal) on power-up only. This design keeps the device in RESET while the power supply stabilizes. With these two timers on-chip, most applications need no external RESET circuitry. SLEEP mode offers a very low current powerdown mode. The user can wake-up from SLEEP through external RESET, Watchdog Timer Time-out or through an interrupt. Several oscillator options are provided to allow the part to fit the application. The RC oscillator option saves system cost while the LP crystal option saves power. A set of configuration bits are used to select the various options.

1. OSCILLATOR TYPES The PIC16F877A can be operated in four different oscillator modes. The user can program two configuration bits (FOSC1 and FOSC0) to select one of these four modes: 1. LP Low Power Crystal 2. XT Crystal/Resonator 3. HS High Speed Crystal/Resonator 4. RC Resistor/Capacitor 2. RESET The PIC16F877A differentiates between various kinds of RESET: 1. Power-on Reset (POR) 2. MCLR during normal operation 3. MCLR during SLEEP 4. WDT Reset (during normal operation) 5. WDT Wake-up (during SLEEP)


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POWER ON RESET (POR)

A Power-on Reset pulse is generated on-chip when VDD rise is detected (in the range of 1.2V - 1.7V). To take advantage of the POR, just tie the MCLR pin directly (or through a resistor) to VDD. This will eliminate external RC components usually needed to create Power-on Reset. A minimum rise time for VDD must be met for this to operate properly. When the device starts normal operation (exits the RESET condition), device operating parameters (voltage, frequency, temperature, etc.) must be met to ensure operation. If these conditions are not met, the device must be held in RESET until the operating conditions are met.

2.2

POWER-UP TIMER (PWRT)

The Power-up Timer (PWRT) provides a fixed 72 ms nominal time-out (TPWRT) from POR. The Power-up Timer operates on an internal RC oscillator. The chip is kept in RESET as long as the PWRT is active. The PWRT delay allows the VDD to rise to an acceptable level. A configuration bit, PWRTE, can enable/disable the PWRT. The operation of the PWRTE bit for a particular device. The power-up time delay TPWRT will vary from chip to chip due to VDD, temperature, and process variation.

3

INTERRUPTS

The PIC16F877A has 4 sources of interrupt: External interrupt RB0/INT pin TMR0 overflow interrupt PORTB change interrupts (pins RB7:RB4) Data EEPROM write complete interrupt The interrupt control register (INTCON) records individual interrupt requests in flag bits. It also contains the individual and global interrupt enable bits. The global interrupt enable bit, GIE (INTCON), enables (if set) all unmasked interrupts or disables (if cleared) all interrupts. Individual interrupts can be disabled through their corresponding enable bits in INTCON register. But GIE is cleared on RESET. The “return from interrupt” instruction, RETFIE, exits interrupt routine as well as sets the GIE bit, which re-enables interrupts. The

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RB0/INT pin interrupt, the RB port change interrupt and the TMR0 overflow interrupt flags are contained in the INTCON register. When an interrupt is responded to, the GIE bit is cleared to disable any further interrupt, the return address is pushed onto the stack and the PC is loaded with 0004h. For external interrupt events, such as the RB0/INT pin or PORTB change interrupts, the interrupt latency will be three to four instruction cycles. The exact latency depends when the interrupt event occurs. The latency is the same for both one and two cycle instructions. Once in the Interrupt Service Routine, the source(s) of the interrupt can be determined by polling the interrupt flag bits. The interrupt flag bit(s) must be cleared in software before re-enabling interrupts to avoid infinite interrupt requests.

5.3.3

CRYSTAL OSCILLATOR

In crystal oscillators, the usual electrical resonant circuit is replaced by a mechanically vibrating crystal. The crystal (usually quartz) has a high degree of stability in holding constant at whatever frequency the crystal is originally cut to operate. The crystal oscillators are, therefore, used whenever great stability is needed, for example, in communication transmitters, and receivers, digital clocks etc. A quartz crystal exhibits a very important property known as piezo-electric effect. When a mechanical pressure is applied across the faces of the crystal, a voltage proportional to the applied mechanical pressure appears across the crystal. Conversely, when a voltage is applied across the crystal surfaces, the crystal is distorted by an amount proportional to the applied voltage. An alternating voltage applied to a crystal causes it to vibrate at its natural frequency. Besides quartz, the other substances that exhibit the piezo-electric effect are Rochelle salt and tourmaline. Rochelle salt exhibits the greatest piezoelectric effect, but its applications are limited to manufacture of microphones, headsets and loudspeakers. It is because the Rochelle salt is mechanically the weakest and strongly affected by moisture and heat. Tourmaline is most rugged but shows the least piezo-electric effect. Quartz is a compromise between the piezoelectric effect of Rochelle salt and the mechanical strength of tourmaline. It is inexpensive and readily available in nature. It is mainly the quartz crystal that is used in radio-frequency (RF) oscillators.


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Fig 80: Electronic oscillator chip

For use in electronic oscillators, the crystal is suitably cut and then mounted between two metal plates, as shown in fig (a). Although the crystal has electromechanical resonance but the crystal action can be represented by an electrical resonance circuit, as shown in fig. (b). The crystal actually behaves as a series RL-C circuit in parallel with CM where CM is the capacitance of the mounting electrodes. Because the crystal losses, represented by R, are small the equivalent crystal Q is high-typically 20,000. Values of Q upto 106 can be obtained by making use of crystals. Because of presence of CM, the crystal has two resonant frequencies. One of these is the series resonant frequency fs at which 2 ∏fL = 1/2 ∏fC and in this case the crystal impedance is very low. The other is parallel resonance frequency fp which is due to parallel resonance of capacitance CM and the reactance of the series circuit. In this case crystal impedance is very high. The impedance versus frequency curve of the crystal is shown in figure. In order to use the crystal properly it must be connected in a circuit so that its low impedance in the series-resonant operating mode or high impedance in the antiresonant or parallel resonant operating mode is selected.

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Two resonant frequencies are given by the expressions Series resonant frequency, fs = 1/2 ∏√LC Parallel resonant frequency, FP = 1/2∏√[1 + C/CM] / LC It appears that fp is higher than fs but the two frequencies are very close to each other. It is due to the fact that the ratio C/CM is very small. To stabilize the frequency of an oscillator, a crystal may be operated at either its series or parallel resonant frequency.

Fig 81: Operation of Oscillator under Resonance

To excite a crystal for operation in the series-resonant mode it may be connected as a series element in a feedback path, as shown in figure. In this mode of operation the crystal impedance is the smallest and the amount of positive feedback is the largest. Resistor R1, R2 and RE provide a voltage-divider stabilized dc bias circuit, the capacitor CE provides ac bypass of the emitter resistor R^ and the radio-frequency coil (RFC) provides for dc bias -while decoupling any ac signal on the power lines from affecting the output signal. The voltage feedback sigrTal from the collector to the base is maximum when the crystal impedance is minimum (that is, The series-resonant mode). The coupling capacitor Cc has negligible impedance at the circuit operating frequency but blocks any dc between collector and base. The circuit shown in figure is generally called the Pierce crystal. The resulting circuit frequency of oscillations


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is set by the series resonant frequency of the crystal. Variations in supply voltage, transistor parameters, etc. have no effect on the circuit operating frequency which is held stabilized by the crystal. The circuit frequency stability is set by the crystal frequency stability, which is good. Crystal oscillators must be designed to provide a load capacitance on the crystal as per specifications listed by the manufacturer. This requirement is essential for obtaining oscillations at the specified frequency. It is also important from the point of view of limiting the power supplied to the crystal to the specified maximum. Too much crystal power causes distortion in the oscillator waveform. It also causes overheating of the crystal, consequently rendering the resonant frequency unstable. More important is that the thin-plated electrodes may be melted off an overdriven crystal, destroying the device. Typical maximum drive levels for plated crystals varies from 2 m W to 10 m W. The maximum permissible drive power limits the ac voltages that may be applied across the crystal and consequently affects the design of oscillator circuits. Crystal manufacturers usually specify the resistance of individual crystal, as well as maximum drive power. From these two, the maximum crystal ac voltage may be determined by using the relation P = V2/R.

5.3.4

LIGHT EMITTING DIODE (LED)

A light-emitting diode (LED) is a semiconductor device that emits visible light when an electric current passes through it. The light is not particularly bright, but in most LEDs it is monochromatic, occurring at a single wavelength. The output from an LED can range from red (at a wavelength of approximately 700 nanometers) to blue-violet (about 400 nanometers). Some LEDs emit infrared (IR) energy (830 nanometers or longer); such a device is known as an infraredemitting diode (IRED). An LED or IRED consists of two elements of processed material called P-type semiconductors and N-type semiconductors. These two elements are placed in direct contact, forming a region called the P-N junction. In this respect, the LED or IRED resembles most other diode types, but there are important differences. The LED or IRED has a transparent package, allowing visible or IR energy to pass through. Also, the LED or IRED has a large PN-junction area whose shape is tailored to the application. Benefits of LEDs and IREDs, compared with incandescent and fluorescent illuminating devices, include:

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Low power requirement: Most types can be operated with battery power supplies.



High efficiency: Most of the power supplied to an LED or IRED is converted into radiation in the desired form, with minimal heat production.



Long life: When properly installed, an LED or IRED can function for decades.

Typical applications include:

5.4



Indicator lights: These can be two-state (i.e., on/off), bar-graph, or alphabetic-numeric readouts.



LCD panel backlighting: Specialized white LEDs are used in flat-panel computer displays.



Fibre optic data transmission: Ease of modulation allows wide communications bandwidth with minimal noise, resulting in high speed and accuracy.



Remote control: Most home-entertainment "remotes" use IREDs to transmit data to the main unit.



Opto isolator: Stages in an electronic system can be connected together without unwanted interaction. DRIVER CIRCUIT

Fig 82: Driver circuit onboard implementation


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IRS2110 MOSFET DRIVER

The IRS2110 are high voltage, high speed power MOSFET and IGBT drivers with independent high-side and low-side referenced output channels. Proprietary HVIC and latch immune CMOS technologies enable ruggedized monolithic construction. Logic inputs are compatible with standard CMOS or LSTTL output, down to 3.3 V logic. The output drivers feature a high pulse current buffer stage designed for minimum driver cross-conduction. Propagation delays are matched to simplify use in high frequency applications. The floating channel can be used to drive an N-channel power MOSFET or IGBT in the high-side configuration which operates up to 500 V or 600 V.

Fig 83: MOSFET/ IGBT turn on and off

Driving a gate as shown in figure 84, driving a gate consists of applying different voltages: 15V to turn on the device through S1, and 0V to turn off the device through S2. A remarkable effect can be seen in both the turn-on and turn-off switching waveforms; the gate voltage exhibits a “step”, remaining at a constant level while the drain voltage rises or falls during switching. The voltage at which the gate voltage remains during switching is known as the Miller voltage, Vgm.

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In most applications, this voltage is around 4 to 6V, depending on the level of current being switched. This feature can be used to control the switching waveforms from the gate drive.

5.4.1.1 MOSFET AND IGBT TURN-ON / TURN-OFF

Fig 84: MOSFET/ IGBT turn on characteristics.

Fig 85: MOSFET/ IGBT turn off characteristics.


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When turned on under the same conditions, IGBTs and MOSFETs behave in exactly the same way, and have very similar current rise and voltage fall times see figure 3. However, at turn-off, the waveforms of the switched current are different, as shown in figure 4. At the end of the switching event, the IGBT has a “tail current” which does not exist for the MOSFET. This tail is caused by minority carriers trapped in the “base” of the bipolar output section of the IGBT causing the device to remain turned on. Unlike a bipolar transistor, it is not possible to extract these carriers to speed up switching, as there is no external connection to the base section, and so the device remains turned on until the carriers recombine naturally. Hence the gate drive circuit has no effect on the tail current level and profile. The tail current does however increase significantly with temperature.

5.4.1.2 IGBT TURN-OFF LOSSES The turn-off of an IGBT can be separated into two distinct periods, as shown in figure 5. In the first period, its behaviour is similar to that of a MOSFET. The increase in drain voltage (dV/dt) is followed by a very fast fall of the switched current. Losses in this “dV/dt” period depend mainly on the speed of the voltage increase, which can be controlled by a gate drive resistor. The second “tail current” period is specific to the IGBT. As this period occurs while there is already a large voltage across the device, it causes losses at each turn-off. The total turn-off losses are shown in figure 5 by the shaded area.

5.4.1.3 FROM GATE DRIVE TO SWITCHING Speeding up turn-off The power involved in these two types of switching losses is linked to the switching frequency. Turn-off losses become critical when operating at high frequencies. In this case, the dV/dt can be increased (and hence losses reduced) by decreasing the size of the gate drive resistor Rg, which will allow the gate to charge more quickly. The turn-off losses are proportional to the size of the gate resistor However, it should be remembered that IGBT tail current losses are completely independent of the value of the gate resistor. Even though the tail current is constant, the losses in a system are often predominantly due to dV/dt, because the value of the gate resistance is often too high. In the example of figure 7, the total losses per cycle are reduced from 13mJ to 4mJ by decreasing the gate resistance from 100Ω to 10Ω.

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Reducing dV/dt at turn-off Conversely, in low frequency applications, fast switching waveforms can cause problems in the form of EMI. A gate driven switch can be used to reduce the amount of EMI, by slowing down the switching speed. This is particularly useful in applications where the mains phase angle is controlled. The dV/dt can be expressed as: dV/dT = Vgm /(Rg . Ciss) where Vgm (the Miller gate voltage) is around 6V, Crss is the equivalent gate-drain capacitance and Rg is the value of the gate resistor at turn-off. This can be achieved using a large gate resistor to make the gate charge more slowly and hence increase the dV/dt time. Throughout the dV/dt period, the voltage across the gate resistor is equal to the Miller voltage (Vgm), and for a short time the power switch operates in linear mode. In this example, with a STGP10N50 IGBT (Crss ≈ 40pF) the dV/dt will be around 7.5V/µs. Alternatively, a capacitor can be connected between the gate and collector / source of the device, which increases the capacitance which must be discharged through the gate resistance at turn-off.

5.5

ONBOARD IMPLEMENTATION

For the sake of hardware implementation, simple circuit is developed taking remote mobile towers as load (48V).

Fig 86: Test bed hardware prototype In this hardware, a battery is taken to be constant PV output and a transformer is taken as constant wind power source. Both these power source are given to SEPIC converter for boosting purpose. Input is nearly 18V. Output is boosted to 48V.


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This kit consists of pulse generating unit here it has 5v -9v dc supply in order to convert it to 12v driver unit is used. In the driver unit it consists of MOSFET which is used to convert 5v to 12v supply, it also consists of PIC controller to control the performance of MOSFET

Table 12: Hardware components tabulation S.No NAME RATING PIC CONTROLLER COMPONENTS 1 Capacitor 470 uf, 25V 2 Regulator 12V 3 Capacitor 10 uf, 25V 4 Resistor 330ohm 5 LED 5V 6 Capacitor 0.1 mF 7 Crystal oscillator 10Mhz 8 Resistor 1 kilo ohm 9 PIC16F877A DRIVER BOARD COMPONENTS 1 Driver IC IRS2110 2 Diode IN4007N 3 Capacitor 1000mf 4 Resistor 330 Ohms SEPIC CONVERTER 1

Power MOSFET

IRF840

2

Inductor

3

Capacitor

4 5

Capacitor Diode

1 uH 1000 uF, 100V 4.7 uF, 50V IN4007N

.

TYPE

QUANTITY

Electrolytic L7805 Electrolytic

1 1 1 1 1 2 1 1 1

Ceramic

Electrolytic

NCHANNEL

1 1 1 2

1 2

Electrolytic

1

Electrolytic

1 1

136 5.6

Control Strategies For Hybrid Renewable Energy Systems HARDWARE OUTPUT

From the onboard circuit, we take the result by giving 12V for both PV and wind as source voltage. Wind source is rectified and given to SEPIC converter, while PV output is DC and is straightaway given to SEPIC converter. SEPIC converter takes care of boosting the input voltage to 48V for facilitating power to the 4.7 kilo ohms resistor.

Fig 87: PV (DC) input power source.

` Fig 88: Wind turbine (AC) input power source.


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Fig 89: Output (AC) voltage of resistor

Input AC and DC sources are measured using DSO and are shown above. Output result is taken and shown using digital multimeter.

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CHAPTER - 6

CONCLUSION

6.1

SUMMARY

Thus the modelling of a hybrid wind/PV alternative energy system and connected to HVDC grid using a DC–DC converter is controlled using ANFIS controller. The main part focuses on the modelling of different energy systems and the corresponding control scheme development. Hardware implementation of SEPIC converter with both input sources is done as prototype for small load and the same could be implemented on a large scale if needed. Special emphasis is laid on the modelling ANFIS control of SEPIC converter based Hybrid system in this book. The conclusions are summarized below. 

Environmentally friendly and sustainable alternative energy systems will play more important roles in the future electricity supply.



Photovoltaic power generation, variable speed wind energy conversion systems and fuel cells. These dynamic models are suitable for both detailed fast transient and large time scale performance evaluation studies.



A hybrid wind/PV system is proposed in this dissertation. Wind and PV are the primary power sources of the system. The different energy sources in the system are integrated through a DC link bus.



The simulation model for the proposed hybrid wind/PV energy system and ANFIS control has been developed successfully using MATLAB/Simulink. Simulation studies have been carried out to verify the system performance under different scenarios, and the results show that the overall power management strategy is effective and the power flows among the different energy sources and the load demand is balanced successfully.

140 6.2

Control Strategies For Hybrid Renewable Energy Systems FUTURE WORK

The future work will be to model the proposed hybrid system using ANFIS combined with Genetic algorithm, and to design the proposed hybrid system and implement in hardware. Also, the system has to be extended to higher ratings and solve for the synchronization issues.

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