ISSC 2014 / CIICT 2014, Limerick, June 26-27
Fuzzy Logic Based Intelligent Energy Monitoring and Control for Renewable Energy Krishna K Panduru1, Daniel Riordan2, Joseph Walsh3 Intelligent Mechatronics and RFID Institute of Technology Tralee, Tralee, Ireland Email:
[email protected] [email protected] 3
[email protected]
Abstract — Renewable energy is an essential area of development in many countries and has attracted a great deal of investment. The use of micro-generator wind turbines is encouraged for individuals to set up their own power generator which will create a positive environmental impact. Power generated from a micro-generator can also be exported back to the national grid with many micro-generation schemes available. The export capacity is usually restricted by these schemes, which require resistive load banks (energy dumps) to be employed to limit the export capacity back to the grid. This leads to energy wastage and resource in-efficiency. To overcome this problem there is a need to measure levels of electricity generation and facility power requirements and provide advanced control. In this paper, we will present a singleton type-1 Fuzzy logic system which monitors the energy generated by a wind turbine, and controls distribute it efficiently power loads which are required and send the excessive energy back to the grid, while avoiding energy dumping. The complete fuzzy logic control system has been developed and performances have been evaluated. Keywords - Fuzzy logic, renewable energy, micro-generation scheme, wireless network
I.
INTRODUCTION
Recently, renewable sources of energy have been a key part in the development of future power grids. Ireland lacks fossil fuel resources and electricity is generated from imported coal or natural gas [1]. By observing climatic changes from the year 1961 to 2000. A simulation for the year 2021 – 2060 has been performed using controlled methods of analysis in [2] which predicts favourable conditions for wind energy farms in Ireland. The total installed wind capacity in the year 2012 in Ireland is 1,827MW [3] and wind power has accounted for 39% of the added renewable power production in the year 2012 [4]. Ireland’s Electricity Supply Board offers a micro-generation scheme which allows customers to produce their own electricity and export the surplus back to the grid. The suggested maximum export capacity (MEC) is limited to 6kW for a single phase connection and 11kW for a three phase connection [5]. In order to limit the maximum export capacity customers take advantage of inverters to charge batteries. When these batteries are completely charged, resistive load banks are used to dump excess power, as it cannot be accepted by the grid. In order to maximise the use of energy within the facility and control the maximum export capacity, an
intelligent system is required which can monitor the energy flow and distribute it to devices. The concept of fuzzy logic was first introduced by Dr. Lofti Zadeh in the year 1965 [6] with which the concept of linguistic variable became very important in fuzzy logic [7]. The fuzzy logic controller is based on approximate reasoning rather than precise control mechanism. Fuzzy logic has the capability to reason and make rational decisions in any given environment [8]. Fuzzy controllers use a simple linguistics approach which converts crisp numerical values into linguistic variables. The linguistic variables for an input membership functions, for example time of the day is taken as “morning, afternoon, evening and night” and for output membership function to control the cooling effect as “low, medium and high”. Another advantage of fuzzy controller is it depends on membership functions and a set of rules which govern the control system, thus no mathematical model is required in the system. Recent developments in fuzzy logic control have made it possible to apply this method to many real work applications. Industrial applications include refrigerator temperature control [9], aircraft engine control [10], automatic train operations [11], personal diet recommendation systems [12], elevator control system [13], camera focus systems and vehicle transmission systems [10].
Section II describes the fuzzy logic controller which is implemented in this system. Section III describes the different hardware nodes designed and implemented. Section IV will provide an overview of the methodology. Section V presents the results obtained from the system developed and the surface controls obtained by the fuzzy logic controller. Section VI concludes the paper and future work which could be implemented to scale the system.
II.
the system [14]. As Mendel in [15] pointed out that “consequently, defuzzification is an art rather than science...”
III.
FUZZY LOGIC CONTROLLER
The block diagram for the fuzzy logic controller is shown in Figure 1. The fuzzy logic controller has four modules. Each module is described in the following paragraphs.
a) Power Measurement Node The power measurement node in Figure 2 is placed near the utility meter which measures the current flow between the power grid and the facility. It comprises of a microcontroller which measures voltage and current using a non-invasive current sensor. Both voltage and current measurements are required to determine the direction of current flow. This node also acts as central node which controls the outputs on the other nodes on the network using fuzzy logic controller.
b) Wind Turbine Control Node
Figure 1: Fuzzy Logic Controller block diagram
The wind turbine node in Figure 3 is connected to the wind turbine and contains a GSM module and a high power relay. The high power relay is used cut off the power in case of adverse weather conditions. The GSM module allows user to communicate with the end user, relay the status of the system and obtain local time from the GSM network which will be used as one of the important input for our fuzzy system.
Fuzzification is a process which converts crisp inputs to linguistic variables or values. Since fuzzy logic controller is based on fuzzy sets. The antecedents and consequents for our fuzzy system are assumed as a trapezoid membership functions. Due to its computational efficiency triangle and trapezoid membership functions are used extensively in real-time applications. Inference engine is known as the heart of fuzzy logic controller. Using unions or intersection the fuzzy inference engine obtains the set of rules fire in the fuzzy system. Norms can be used as intersection operation and co-norms can be used as union operation. An individual rule based inference engine has been implemented in our current system; the output of this fuzzy inference engine is an intersection of the individual input fuzzy sets. A fuzzy control rule is implemented by fuzzy implication where the logical connective AND is implemented by the minimum operator. We can use any norm to design the logical AND operator in fuzzy logic as shown in equation (1). (
)
WIRELESS NODES
In our experiment we have employed three kinds of nodes each of them equipped with a Zigbee module which can wirelessly communicate. The nodes comprises of a star network. The star network is easy to implement in Zigbee which can isolate slave nodes and has better performance [16].
(
c) Appliance Node The appliance node in Figure 4 consists of a set of sensors and control relay depending on the type of appliance to which it is connected. In our case node N2 has a temperature sensor to measure temperature of water and a control relay to turn the heater on or off.
)
(1)
Where, R1 is the rule number. x0, x1… xn, z are input variables, A0, A1…An, B, are fuzzy variables. Defuzzification is the process of converting fuzzy output variables to crisp outputs. A number of mathematical procedures have been suggested for defuzzification. The procedure for selecting the right defuzzification method is not particularly defined, but it depends on the type of application and the computational power available in
Figure 2: Power measurement node
-1 to 1. When a load (such as a micro generator) generates power we obtain a negative power factor [18].
Figure 3:Wind turbine node
IV.
Figure 4: Appliance node Figure 5: voltage and current from 2kW water heater measured from source
METHODOLOGY
In this section we will be talking about the design involved in obtaining the apparent and real power from the renewable energy source, the membership functions and set of fuzzy rules used. The power measurement node measures voltage and current flow through the system, the microcontroller takes more than 70 readings per cycle for determining instantaneous voltage and current. Then based on values obtained apparent power, real power and power factor are calculated from the equations (2) – (6). Figure 5 and Figure 6 shows the voltage and current obtained by the microcontroller. Real power is the power used by a device to produce useful work [17]. It is the product of definite integral of voltage and the current divided by the number of samples as seen in equation (2). ∑
(
)
(2)
Apparent power is the product of the root mean squared values of voltages and current is a single cycle. RMS value of voltage or current is defined as the square root of the mean values of the squares of the instantaneous voltages or current on a single cycle. Equation (3) and (4) shows how to calculate the RMS values for voltage and current respectively. √ (
)
Figure 6: voltage and current from 2kW water heater measured from load
The microcontroller uses a 16-bit SAR ADC to digitize the sensor input and the result is stored in a floating point number. The accuracy of real power and apparent power is very important to calculate the power flow. Since we are using fuzzy logic, we can tune our membership functions with respect to crisp inputs to produce a reliable output.
(3) Figure 7: Fuzzy Logic System for water heater node
√ (
)
(4) (5)
⁄
(6)
Power factor determines the real power flow to the load in. It is a pure number value ranging from
For the antecedents in our fuzzy system we assume that the input for ‘time’ can be in the range of 0 to 1440 minutes. Based on the maximum export capacity the variable ‘power’ is set at 6kW. And 70°C is set for maximum water ‘temperature’. And the consequents for controlling the relay, the output can be in the range of 0 to 100 divided into RELAY_ON and RELAY_OFF. Figure 8 - Figure
11 illustrate the antecedents and consequents designed for the fuzzy logic controller. The inferred fuzzy output from the inference engine is solved by height defuzzification method as seen in equation (7). ∑ ∑
(7)
Figure 8: Input variable for (temperature)
Figure 9: Input variable for (time)
Table 1: Rules for water heater appliance node
TIME
POWER
NIGHT NIGHT NIGHT NIGHT NIGHT NIGHT MORNING MORNING MORNING MORNING MORNING MORNING AFTERNOON AFTERNOON AFTERNOON AFTERNOON AFTERNOON AFTERNOON EVENING EVENING EVENING EVENING EVENING EVENING
GOOD GOOD GOOD EXCESS EXCESS EXCESS GOOD GOOD GOOD EXCESS EXCESS EXCESS GOOD GOOD GOOD EXCESS EXCESS EXCESS GOOD GOOD GOOD EXCESS EXCESS EXCESS
WATER TEMP COLD WARM HOT COLD WARM HOT COLD WARM HOT COLD WARM HOT COLD WARM HOT COLD WARM HOT COLD WARM HOT COLD WARM HOT
HEATER LOAD R_ON R_ON R_OFF R_ON R_ON R_OFF R_OFF R_OFF R_OFF R_ON R_ON R_OFF R_OFF R_OFF R_OFF R_ON R_ON R_OFF R_OFF R_OFF R_OFF R_ON R_ON R_OFF
Table 2: Rules for resistive load
Figure 10: Input variable for (power)
DEVICES
POWER
D_ON D_ON D_OFF D_OFF
GOOD EXCESS GOOD EXCESS
Many individual tests have been performed to verify the fuzzy rules which are fired in the system. Based on the precision of current and temperature sensor, we have decided to use a singleton input for our fuzzy system to increase computational efficiency.
V.
Figure 11: Output variable for (water heater)
For our appliance node N2, the best condition to heat water would be during night to take advantage of cheaper electricity rates or if there is excessive power flowing back to the grid anytime of the day and when the water is cold. And turn off the heater when the water is hot. Based on these factors in mind, fuzzy rules have been formulated. Table 1 and Table 2 show the fuzzy rules implemented in the system for water heater node and resistive dump node.
RESISTIVE LOAD R_OFF R_ON R_OFF R_ON
RESULTS AND DISCUSSIONS
A simulation of fuzzy control surfaces has been realized in MATLAB environment. The fuzzy control surfaces shown in Figure 12 - Figure 14 illustrate smooth transitions of outputs in the relay. Since, the antecedents in the fuzzy controller are obtained directly by the sensors and hard-coded values for the input time in minutes; the system doesn’t suffer from any external noise or uncertainty. Thus no uncertainty has been introduced in the experimental results.
the evening with the water being warm, the relay is turned on. Condition 5 shows when the water is completely heated in the evening and the relay is off. Condition 7 and 8 shows the output when the rules are in a transient condition. Table 3: Results obtained by the Fuzzy Inference System
Sl.no
Figure 12: Control surface for time and temperature input membership
1 2 3 4 5 6 7
VI.
Figure 13: Control surface for power and temperature input membership
Figure 14: Control surface for power and time input membership
The practical experiments were done using a 10kW wind turbine and the above described nodes which has fuzzy logic controller running in the power node. Table 3 shows the results obtained from different conditions. The first three conditions in the table show the water being heated in the morning and the relay turns off when the water is completely hot. Condition 4 shows excessive power being dumped in
Time in minutes 181 210 358 1208 1305 798 392
Power 19.6 33.2 33.1 33.5 43.2 28.7 25.6
Water temperature 22.1 22.5 68.3 50.2 78.9 65.4 62.6
Relay output 80 80 26.67 80 20 35.32 46.67
CONCLUSION AND FUTURE WORK
In this paper, we have presented a type-I fuzzy logic system which can monitor energy, control and distribute the excess energy to devices in the facility and finally use a resistive load bank in the case where power has to be dumped. The control surfaces show a smooth transition which results in a better control system when compared to IF-ELSE style control systems. Implementing a fuzzy logic controller is a completely different approach for a control problem. Bivalent logic such as IF-ELSE style control cannot handle imprecision. For this reason it is not suited for a control system with imprecision and vagueness. Fuzzy control system is adaptive in nature and has the ability to realize control for each input state. Fuzzy logic focuses on the problem itself rather than working on a complex mathematical model to control the system. Modifying the current control system can be achieved by changing the values in the fuzzy membership function or the fuzzy rules. Adding more appliance nodes in the system will be much easier when using fuzzy logic as no complex mathematical model is involved. A domain expert can easily design additional fuzzy membership functions which constitute of simple linguistic variables for the fuzzy antecedents and consequents, then design rules for the new appliance added in the system. In terms of hardware, the accuracy and sampling speed for voltage and current are limited by the analog read function. Implementing an external analog to digital converter which can obtain more accurate inputs would be more beneficial. In our future work, we will implement a secondary fuzzy logic controller in each of the appliance nodes which will take charge in case of an inoperable network or failure of obtaining any fuzzy input from the system. We are also considering implementing a fuzzy logic based intelligent wireless sensor network to optimise the power consumption in the network [19].
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