Implementation of Fuzzy Logic based Maximum Power Point Tracking

Proc. of Int. Conf. on Control, Communication and Power Engineering, CCPE

Implementation of Fuzzy Logic based Maximum Power Point Tracking in Photovoltaic System Dr.E.A.Jasmin1 and Jerin James2 1

Associate Professor, Dept.of Electrical & Electronics, Govt.Engg.College, Thrissur, India [email protected] 2 Student, Dept.of Electrical & Electronics, Govt.Engg.College, Thrissur, India [email protected]

Abstract— Meeting the increasing energy demand has turned to be a challenging task in the power sector. The utilization of the renewable energy sources like wind, solar etc. are being much focused to balance the demand with the power sources. In the coming days at least 50% of the energy demand has to be met with the renewable sources since the availability of fossil fuels will be significantly less. Out of these renewable sources Photovoltaic is the most focused one due to the abundance compared to other renewable sources. Since the cost of installation systems is more, tapping maximum energy from an installed system is a technical and economical challenge. For the same, a number of Maximum Power Point Tracking (MPPT) methods have been envisaged by the technical community, each of which has its own advantages and limitations. Due to the ill defined nature of the control environment, Fuzzy Logic is found to be one suitable method to implement an MPPT system for a photovoltaic source. In this paper the implementation of a Fuzzy logic controller used for Maximum Power Point Tracking of PV system is proposed which is a very useful one for micro grid structure in the islanded mode of operation. The implementation is done through a DSPACE interface. Index Terms— Renewable energy, Photovoltaic, MPPT, Fuzzy Logic

I. INTRODUCTION Due to increase in population, urbanization and industrialization etc., the energy demand is increasing day by day. At the same time, the conventional sources of energy are depleting. This results in inflation and energy shortage. Therefore other systems based on non-conventional and renewable sources are being focused by each and every nation of the world. Renewable energy sources play an important role in electricity generation. Photovoltaic (PV) systems are the one which is more significant in this category due to the abundance of solar power. To get maximum energy from the PV system, technologies are being developed to track the Maximum Power Point of the system at each instant. The Maximum Power Point Tracking (MPPT) devices or controllers will make the system to tap maximum energy so that technological and economical efficiency is achieved. MPPT controllers are now an essential element in PV system. A significant number of MPPT control methods have been elaborated since the seventies, starting with simple techniques such as voltage and current feedback based MPPT to more improved power feedback based MPPT such as the perturbation and © Elsevier, 2014

observation (P&O) technique [1],[2] or the incremental conductance technique [3]. Also a number of soft computing techniques are also being developed to get the Photovoltaic system to work at Maximum Power Point. This includes Neural Network, Fuzzy Logic etc. [6 -8]. Different optimization based methods are proposed by several researchers [9 – 12]. The basic idea is to control the switching pulses of the DC – DC converter associated with the PV system. In order to incorporate the photovoltaic system as a generating source in micro grid structure, a reliable implementation technique of an efficient MPPT system is very essential In this paper, an intelligent control technique using fuzzy logic control is associated to an MPPT controller in order to improve energy conversion efficiency. Figure 1 shows the block diagram of proposed system.

Figure 1 Block diagram of MPPT

Section II describes about photovoltaic system and Maximum power point tracking method and various methods for MPPT are described in section III. Proposed fuzzy logic controller is explained in section IV and the simulation results are presented in section V Section VI gives the hardware implementation details and conclusion is followed in section VII. II. STRUCTURE OF PHOTVOLTAIC SYSTEM The power produced by a single PV cell is not enough for general use. So PV cells are combined together in series to increase the voltage. Several of these series strings of cells may be connected in parallel to increase the current as well. These interconnected cells and their electrical connections are then sandwiched between a top layer of glass or clear plastic and a lower level of plastic or plastic and metal. An outer frame is attached to increase mechanical strength, and to provide a way to mount the unit. This package is called a “module”. The PV cells in a module can be wired to any desired voltage and current. The amount of current produced is directly proportional to the cell size, conversion efficiency, and the intensity of light. Generally commercial modules consist of 32 or 72 cells [13].Figure 2 shows the equivalent model of the PV cell is shown [15].

Figure 2 Equivalent model of PV cells

The current I is given by the following equation:  =  −  −  =  −  

   

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−

 

(2.1)

The devices that have been analysed so far are solar photovoltaic cells used in the conversion of solar energy into electricity, these devices are made from silicon, it is, monocrystalline, polycrystalline or amorphous. The PV cell output power can be given by P = IsV − IV[exp( qV/nkT ) − 1].

(2.2)

A. Electric characteristic of the PV cell When the sunlight intensity and the temperature are certain, the characteristic current of the PV cell can be expressed as (2.2), indicating the relation among output power, voltage, and current of the PV cell under sunlight intensity, solar spectra distribution, and PV cell temperature. The standard conditions are sunlight intensity S = 1000W/m2, spectra AM1.5, and PV cell temperature T = 25◦C. In this case, the maximum output power of the PV cell can be called peak power, with a unit of Wp. The power produced by the cell in Watts can be easily calculated along the I-V sweep by the equation P=IV. At the ISC and VOC points, the power will be zero and the maximum value for power will occur between the two. The voltage and current at this maximum power point are denoted as VMP and IMP respectively.

Figure 3 Output characteristics of the PV array

According to Figure. 3, the volt-ampere characteristic of the PV cell is highly nonlinear, so it is neither a constant voltage source nor a constant current source, and cannot supply greater power for load. Its volt-watt characteristic is also a nonlinear process, even under different times and different conditions. The changing relation between volt and ampere or watt cannot be estimated. According to theoretical derivation and practical experience, the PV array can be influenced by sunlight intensity, circumstance temperature, putting position, putting angle, and cell junction temperature, among others. With the change in environment condition, the output characteristic of the PV array changes, and its output maximum power point changes as well. B. Impact of solar radiation and temperature on V-I characteristic curve of Photovoltaic Module The current-voltage (I-V) curve is based on the module being under standard conditions of sunlight and module temperature. It assumes there is no shading on the module. Standard sunlight conditions on a clear day are assumed to be 1000 watts of solar energy per square meter (1000 W/m2or lkW/m2). This is sometimes called "one sun," or a "peak sun." Less than one sun will reduce the current output of the module by a proportional amount. For example, if only one-half sun (500 W/m2) is available, the amount of output current is roughly cut in half (Figure 4).

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Figure 4 Impact of solar radiation on V-I characteristics curve

Figure 5 Impact of temperature on V-I characteristic curve

Module temperature affects the output voltage inversely. Higher module temperature will reduce the voltage every one Celsius degree rise I temperature. Higher module temperatures will reduce the voltage by 0.04 to 0.1 volts for every one Celsius degree rise in temperature (0.04V/0C to 0.1V/0C). In Fahrenheit degrees, the voltage loss is from 0.022 to 0.056 volts per degree of temperature rise (Figure 5). III. MAXIMUM POWER POINT TRACKING When a PV module is directly coupled to a load, the PV module’s operating point will be at the intersection of its I–V curve and the load line which is the I–V relationship of load. For example, a resistive load has a straight line with a slope of 1/Rload as shown in Figure 6

Figure .6 I-V relationship

In other words, the impedance of load dictates the operating condition of the PV module. In general, this operating point is seldom at the PV module’s MPP, the optimal adaptation occurs only at one particular operating point, called Maximum Power Point (MPP) and noted in our case Pmax. In other words, the impedance of load dictates the operating condition of the PV module. In general, this operating point is seldom at the PV module’s MPP, the optimal adaptation occurs only at one particular operating point, called Maximum Power Point (MPP) and noted in our case Pmax. To overcome this problem, it is necessary to add an adaptation device, MPPT controller with a DC–DC boost converter between the source and the load. Furthermore the location of the MPP in the I–V plane is not known beforehand and always changes dynamically depending on irradiance and temperature Many MPPT control techniques have been conceived 549

for this purpose during these last decades such as Perturb and observe algorithm, Incremental conductance algorithm, Parasitic capacitances, Constant voltage control, Constant current control etc. [1 -12]. These control strategies basically include three categories: • Voltage feedback based methods which compare the PV operating voltage with a reference voltage in order to generate the PWM control signal of the DC–DC converter. • Current feedback based methods which use the PV module short circuit current as a feedback in order to estimate the optimal current corresponding to the maximum power. • Power based methods which are based on iterative algorithms to track continuously the MPP through the current and voltage measurement of the PV module . IV. FUZZY LOGIC CONTROLLER Due to the uncertainty in the control criteria and lack of precise modelling method for tracking the control action, Fuzzy logic is found to be suitable for the tracking of the MPP in PV systems. They have the advantage to be robust and relatively simple to design as they do not require the knowledge of the exact model. They do require in the other hand the complete knowledge of the operation of the PV system by the designer. The system presented here consists of two input variables: error (E) and change in error (CE) and output variable change in duty ratio dD. Figure 7 shows the General diagram of fuzzy logic controller. It have mainly 3 parts Fuzzification, Inference and Defuzzification [18-19].

Figure .7 General diagram of fuzzy logic controller

A. Fuzzification Membership function values are assigned to the linguistic variables, using five fuzzy subsets: NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big). The partition of fuzzy subsets and the shape of membership function, which can adapt shape up to appropriate system, are shown in Figure 8. The value of input error (E) and change of error (CE) are normalized by an input scaling factor. The actual voltage and current of PV array can be measured continuously via on chip A/D converter of 8CC196KB microcontroller and the power can be calculated. We have focused on single input-single output plant in which control is determined on the basis of satisfaction of two criteria relating to two input variables of proposed controller, namely error (E) and change of error (CE), at a sampling instant k. The variable E and CE are expressed as follows: E(k) = ( P(k) – P(k-1) ) / ( I(k) – I(k-1) )

(4.1)

CE(k) = E(k) – E(k-1)

(4.2)

Where P(k) and I(k) are the power and current of the PV array, respectively. Therefore, E(k) is zero at the maximum power point of a PV array. B. Inference method Table 1. shows the rule table of fuzzy controller, where all the entries of the matrix are fuzzy sets of error (E), change of error (CE) and change of duty ratio (dD) of the boost converter. In the case of fuzzy control, the control rule must be designed in order that input variable E has to always be zero. C. Defuzzification Defuzzificaion method for this system is the centre of gravity to compute the output of this FLC which is the duty ratio (cycle). The centre of gravity method is both very simple and fast. 550

Figure .8 Membership function of (a) input E, (b) input CE, (c) output dD TABLE I. FUZZY RULE B ASE CE E NB NS ZO PS PB

NB ZO ZO NS PS PB

NS ZO ZO ZO PS PB

ZO NB NS ZO PS PB

PS NB NS ZO ZO ZO

PB NB NS PS ZO ZO

The Duty ratio, the output of fuzzy logic control is given to PWM which generates pulse to control MOSFET switch in DC–DC converter. The output of fuzzy controller is a fuzzy subset of control. As the plant usually requires a non fuzzy value of control, a defuzzification stage is needed. Defuzzification can be performed normally by two algorithms. Center of Area (COA) and the Max Criterion Method (MCM). The COA is a combine-then defuzzification algorithm that determines the crisp controller output as the center of gravity of the final combined fuzzy set. The final combined fuzzy set is defined by the union of all rule output fuzzy set using the maximum aggregation method. For a sampled data representation, the center of gravity dD, is computed point-wise by  =

∑  ! (). ∑ " ! ()

(4.3)

V. PERFORMANCE OF PROPOSED CONTROLLER MATLAB/SIMULINK is a software package for modelling,simulating and analysing the dyanamic systems.It supports the linear and nonlinear systems modelled in continuous time,sampled time or a hybrid of two.Systems can also be multirate ie, have different parts that are sampled or updated at different rates. For modelling, SIMULINK provides a graphical user interface (GUI) based on building concept. The simulation of the PV system was carried out in MATLAB/SIMULINK Power systems environment. The algorithm used for MPPT controller is based on fuzzy logic. Fuzzy logic controller is modelled using fuzzy toolbox in MATLAB. The entire PV system was simulated and simulation results are verified [14]. From the results it was clearly found that the PV system becomes more efficient when a MPPT controller is included in 551

the system when compared to a PV system without MPPT controller. Hence it has been proved that the designed controller, with the adequate choice of membership functions, can make sure that the MPPT will follow the true MPP and thereby the overall efficiency of the PV system can be improved[16-17] [20].Figure 9,10,11 shows MATLAB simulation block diagrams of PV system model,Solar panel,FLC based MPPT respectively.

Figure. 9 PV system model

Figure. 10 Solar panel

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Figure: 11 FLC based MPPT

A. Simulation Results Figure 12 shows the MPPT using fuzzy logic waveforms of power, current and voltage. Figure 13 shows the switching pulses from the fuzzy logic controller

Figure.12 MPPT using fuzzy logic wave forms

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Figure.13 Switching pulses from Fuzzy logic controller

VI. HARDWARE IMPLEMENTATION AND ANALYSIS The developed Fuzzy logic controller for Maximum Power Point Tracking is implemented for a 30 V Photovoltaic system. Implementation includes the realization of PV module, Fuzzy logic controller using dSPACE, Boost Converter and the Sensing unit. Schematic diagram of proposed method is shown in Figure 14. PV array serves as input power source to the DC-DC converter which is then controlled by square wave switching signals from the dSPACE microcontroller. The duty ratio of the signal is controlled by fuzzy logic to search for the MPP of the PV array. The switching pulses are generated by the controller in real time, sensing the voltage and current. These pulses are interfaced to the DC – DC converter using dSPACE. DS1104 dSPACE controller board is used for giving switching pulses to the boost converter system. dSPACE control cards provide an excellent environment for integrating the MATLAB/ Simulink controllers and therefore widely used in controlling motors and power electronic converters. Here 1N5822 schottky diode, Optocoupler TLP 250, ACS 712 current sensor is used for capturing the real time input to the controller. The sensors periodically senses the input variations and Fuzzy logic controller modifies the switching pulses accordingly. Figure 15 shows the implementation layout of our proposed controller.

Figure.14 schematic diagram for proposed MPPT controller

The pulse output waveforms obtained from the proposed system is given below (Figure 16) The fuzzy logic controller produces the controlled pulses which switches the DC DC converter. The developed prototype was tested for d.c voltages from 5 – 45 V corresponding to the irradiance at different times of the day. The controlled output is found to be at 30V which is the desired constant value. The system is found to give satisfactory performance as far as the Photovoltaic Maximum Power Point tracking is considered. 554

Figure.15 Circuit diagram

Figure. 16 pulse output waveforms

VII. CONCLUSION The proposed fuzzy based MPPT system was simulated using MATLAB and implemented using dSPACE. The output of dSPACE was given to the PWM generator which gave pulses corresponding to change in duty ratio.the output is found to remain constant irrespective of change in irradiance. Thus this system is found more efficient and can be incorporated with the Photovoltaic systmes in a microgrid for their efficient operaion. REFERENCES [1] Henry Shu-Hung Chung,K.KTse, S.Y.RonHui, C.M.Mok, M.T.Ho, “A novel maximum power point tracking technique for solar panels sing SEPIC or Cukconverter”, IEEE Transactions on power electronics,volume 18,issue 3, May 2003 page (s): 717-724.

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