Optimization of New Fuzzy

1. Introduction

Photovoltaic energy is a sort of solar energy that isavailable in almost all parts of the world and has theleast maintenance since it attracts researchers toward thiskind of clean and renewable energy. Despite abundantadvantages, PV module has low energy conversion effi-ciency [1]. To overcome the problem, maximum powerpoint tracking technique is necessary [2, 3].

The sunlight intensity is time variant and sometimeschanges rapidly in a day, because of this, the optimumoperation point of PV module moves from one curve toanother. So the maximum power point tracker must trackthe maximum point as rapidly as possible in order toalleviate the oscillation of output power of PV module [4].Among the proposed methods, intelligent control issurrogated the conventional algorithms like P&O, INCand so on [5-7]. Fuzzy logic control is an intelligentmethod which has simplicity and effectiveness in linearand nonlinear systems. Also this control has high impli-cation in maximum power point tracking in photovoltaicsystems [8]. For example the ref. [9] corroborated that

the FLC reach to maximum power point eight timesbetter than conventional P&O algorithm.

In this paper intelligent control method using newfuzzy logic controller optimized by genetic algorithm isproposed to obtain better performance of energy con-version. The simulation results of optimized FLC com-pared with new and traditional FLC under variableweather conditions.

2. PV cell model and characteristics

2.1. Equivalent model of PV cell

Accurate mathematical model is necessary to representthe electric characteristics of PV module [10]. The con-ventional equivalent circuit of solar cell is expressed byone or two diode whereas representing by a photocurrentsource, parallel diode, shunt resistance (Rsh) and seriesresistance (Rs) as seen in Figure 1. The current source(Iph) models the sunlight energy conversion, the shuntresistance represents the consequence of leaks, the seriesresistant represents the various resistances of connectionsand the diodes model the PN junctions [11].

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Abstract

Maximum power pointtracking (MPPT) is

the main solution to reducethe power loss in the pho-tovoltaic (PV) system whentemperature and solar ir-radiance variation occurs.This paper proposes a newintelligent control techni-que using fuzzy logic con-troller (FLC) and optimi-zation of its parameters byGenetic Algorithm (GA) toobtain the maximum available power of PV module

under unstable conditions.Performance of the con-ventional fuzzy logic con-troller is compared withnew and optimized maxi-mum power point tracker.Simulation results demon-strated better operation ofthe optimized fuzzy logiccontroller under variableweather conditions in com-parison with conventionalFLC.

Keywords: Fuzzy, GeneticAlgorithm, MPPT, Photovoltaic.

Optimization of New FuzzyLogic Controller by Genetic

Algorithm for MaximumPower Point Tracking in

Photovoltaic System

Alireza Rezaei andS. Asghar Gholamian

Babol University of Technology, Faculty of Electrical and Computer Engineering,

Babol, IranEmail: [email protected]

V o l u m e 9 - N u m b e r 1 5 - M a y 2 0 1 3 ( 9 - 1 6 )

ISESCO JOURNAL of Science and Technology

2.Rezaei&Gholamian 2/05/13 8:33 Page 9

The photocurrent generated by the PV module isgiven by equations 1 and 2:

Where V and I is the PV module voltage and current;q is the electronic charge; Is1,2 is saturation current ofdiodes; A1,2 is ideality factor of PV junctions; K isBoltzmann's constant and T the cell temperature.

2.2. Electric characteristics of PV cell

The output power of PV module is dependent on twoparameters, sunlight intensity and PV cell temperature.Solar irradiance has direct relation and temperature hasreverse relation with output power of PV module. Itmeans increasing the sunlight intensity; the output powerrises up. Increasing the temperature; the power comesdown. Figure 2 and Figure 3 show the output charac-teristics of PV module under variable sunlight intensityand different temperatures.

3. MPPT Technique

Figure 4 shows nonlinear characteristic of PV module,load characteristic, and the maximum power point. As itseems there is just an optimum operation point in PVmodule curve. If the electrical load connects to the PVmodule directly and resistance is equal to division ofvoltage into current of the module, then it is possible toobtain maxim power [13]. But if the load resistance isnot equal, reaching to maximum power is impossible.However under variable weather conditions, the MPPmoves from one curve to another so the resistance ofelectrical load must change to obtain maximum powerand this is not executive. Therefore in constant loadsituation an intermediate part is required. This part ismaximum power point tracker. Maximum power pointtracker is a DC/DC converter which is associated withcontrol unit. It is usually embedded between PV moduleand the load [14] as seen in Figure 5.

There are several MPPT techniques as hill-climbing,P&O, Incremental Conductance. These are conventionalmethods and have some drawbacks such as [15]:

l Converging to maximum operation point is slow.l Oscillation of PV power amplitude around MPP is

considerable that cause power losses.l When the irradiance changes quickly, the system

response is slow and moves away from MPP.

To conquer these drawbacks, modern MPPT techniquessuch as Fuzzy Logic Controller, neural network andintelligent method are proposed [16, 17].

Alireza Rezaei and S. Asghar Gholamian / ISESCO Journal of Science and Technology - Volume 9, Number 15 (May 2013) (9-16)

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Figure 1. Equivalent circuit of PV cell [12]

(1)

(2)

Figure 2. I-V and PV characteristics of a PV module for differenttemperature

Figure 3. I-V and PV characteristics of a PV module for variedsunlight intensity


4. Fuzzy Logic Controller

Fuzzy logic controller properly performs in nonlinearsystems. It is on the basis of designer knowledge ratherthan accurate mathematical model [1]. FLC consists offour categories as fuzzification, inference engine, rulebase and defuzzification. In the first section, numericalinput variable are converted into fuzzy variable knownas linguistic variable [1]. Inference engine definescontroller output in order to fuzzified input, rule baseand fuzzy inference methods. Rule base section consistsof “if A and B and C then D” forms. Finally outputlinguistic terms are converted to numerical variable indefuzzification section. Figure 6 shows the fuzzy infe-rence system.

Conventional fuzzy maximum power point trackerconsists of two inputs and on output. But as shown inFigure 7 Vpv is added to input in proposed method toenhance the accuracy of tracking the optimal point.Equations (3-5) express the inputs of FLC; E, CE andVpv. The defuzzification uses center of gravity to computethe output of FLC (∆D) as equation (6).

Figure 8 shows fuzzy membership functions for E,CE, Vpv and ∆D. The terms N,Z,P,L and G respectivelymean negative, zero, positive, little and great. The outputmembership functions are nominated as mf1 to mf9. Theextent of membership functions parameter are {-10, 10},{-10, 10}, {30, 40} for inputs and {-1, 1} for the output.


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Figure 4. Photovoltaic module and load characteristics

Figure 5. Stand-alone photovoltaic system [12]

(3)

(4)

(5)

(6)

Figure 6. Fuzzy inference system [18]

(7-a) [19]

(7-b)

Figure 7. Configuration of MPPT algorithm with Fuzzy LogicController, a) conventional method, b) proposed method

(8-a)


The fuzzy inference is carried out by mamdani'smethod. The control rules are indicated in Table 1.

5. Optimization of Fuzzy Logic Controllerby Genetic Algorithm

Genetic algorithms search the best access to solvethe problem in order to natural selection and geneticstheory. This algorithm produces some random responsesand coding each one as a chromosome. The fitness ofeach chromosome is defined by evaluation of fitnessfunction. Each chromosome has higher value of fitnessis preserved and other is eliminated. Frequently twochromosomes that have a higher value are selected asparents. These parents exchange their genes and generatenew population. Pairs of chromosomes in the new popu-lation are chosen randomly for exchange of geneticmaterial, this is called crossover. After producing twonew chromosomes which replaces its parents, in mutationoperator randomly chosen bits in the offspring areflipped [20]. Here if the terminating state is satisfiedthen the algorithm stops otherwise back to second stageand repeats the further stage. The genetic algorithmstructure is shown in Figure 9.

In our work the purpose is to find the optimummembership function parameters due to generate optimalduty cycle. So determining the fitness function of the GAis the most important part. For each individual chromo-some the fitness value should be determined in thepopulation. To alleviate the output power of PV modulethe fitness function is described in equations (7-8):


12

(8-b)

(8-c)

(8-d)

Figure 8. Membership functions, a) first input, b) second input,c) third input, d) output

TABLE 1. Fuzzy rules

NNZZPPNNZPPPNNZZPP

NNNNNNZZZZZZPPPPPP

input2

Mf4Mf1Mf3Mf1Mf5Mf4Mf4Mf2Mf5Mf8Mf6Mf4Mf7Mf5Mf7Mf6Mf9Mf7

inputinput1

ZGZGZGZGZLZGLZLZLZ

input3

Figure 9. Genetic algorithm structure [21]

(7)

(8)


P (t) ref is the output power of PV module under STC(Standard Test Condition), 1000 w/m2 irradiance andtemperature 25°C. Table 2 summarized the parametersused in GA.

The population consists of a set of individuals. Eachindividual is composed of four chromosomes: E(t),CE(t), V(t), ∆D as described in Table 3. The parameters(C1-C18) indicate the genes in genetic algorithm. Theoptimized membership functions are shown in Figure 10.

6. Simulation and results

Photovoltaic system consists of PV module, MPPTcontroller, DC/DC converter with resistive load andpulse width modulator. “BP SX150” photovoltaic paneland MATLAB software is used for simulation. Table 4provides the following information on BP SX150S PVmodule [22]. DC/DC converter parameter's value isshown in Table 5.


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TABLE 2. Options of genetic algorithm

Population size

generation

Selection method

Fitness scaling

Rate of crossover

Mutation method

Parameter

40

120

Roulette wheel

Top

0.8

Gaussian

Value

TABLE 3. Chromosomes of membership function

Chromosome 1

C1 C2 C3

Chromosome 2

C4 C5 C6

Chromosome 3

C7 C8 C9

Chromosome 4

C10 C11 C12 C13 C14 C15 C16 C17 C18

(10-c)

(10-d)

Figure 10. Optimized membership functions, a) first input,b) second input, c) third input, d) output

(10-a)

(10-b)

TABLE 4. BP SX 150S data sheet [22]

Maximum power (Pmax)

Voltage at Pmax (Vmp)

Current at Pmax (Imp)

Short-circuit current (Isc)

Open-circuit voltage (Voc)

Temperature coefficient of Isc

Temperature coefficient of Voc

Temperature coefficient of power

NOCT

Model

150W

34.5V

4.35A

4.75A

43.5V

(0.065±0.015)%/°C

-(160±20)mV/°C

-(0.5±0.05)%/°C

47±2°C

BP SX 150S

TABLE 5. DC/DC converter configuration

L

Cin

Cout

Switching frequency

168 µH

15 µF

500 µF

25 kHz


The simulation is run under standard test condition.As seen in Figure 11, the proposed fuzzy logic controllerperformance is better than the conventional FLC. Also,the optimized proposed FLC reduce the oscillations ofphotovoltaic module output power.

Figure 12 shows the performance of controllers under500 W/m2 irradiance with 25°C temperature and Figure 13expresses controllers operation under 1000 W/m2 irra-diance with 50°C temperature. It is obvious that outputpower of PV module oscillations decreased by proposedcontroller and grew better by optimized FLC.

Also, the pick-to-pick oscillation of output power ofPV module is compared in Table 6 with different weatherconditions and different MPPT controllers.


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(11-a)

(11-b)

(11-c)

Figure 11. Output power of PV module under 1000 W/m2 irra-diance and 25°C temperature, a) conventional fuzzy controller,

b) proposed controller, c) optimized proposed controller

(12-a)

(12-b)

(12-c)



TABLE 6. Pick-to-pick oscillation of PV output power (watt)

0.16

0.07

0.18

T = 25 °CG = 1 kW/m2

T = 25 °CG = 0.5 kW/m2

T = 50 °CG = 1 kW/m2

conventionalFLC

0.03

0.03

0.09

opt proposedFLC

Controller

Conditions

0.1

0.05

0.18

proposedFLC


Also, performance of optimized fuzzy logic controlleris studied under variable solar irradiance and temperaturesimultaneously. It is considered that the solar irradiancechanges from 1000 w/m2 to 600 w/m2 and 600 w/m2 to1000 w/m2 in t=0.1 sec to t= 0.4 sec and t=0.5 sec tot=0.7 sec. Also, temperature changes from 25°C to 20°Cand 18°C in t=0.1, t=0.4 and t=0.7. The PV output power,voltage and current variation under unstable condition isshown in Figure 14.


15

(13-a)

(13-b)

(13-c)



(14-a)

(14-b)

(14-c)

(14-d)

Figure 14. Proposed mppt controller, a) solar irradiance variation,b) temperature variation, c) PV power, d) voltage and current of

PV module



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Conclusion

Maximum power point tracker has a significant rule in PV systems because of low efficiency of PV modules,crisis of energy and incremental cost of fossil fuel. In this paper an intelligent maximum power point tracking tech-nique for photovoltaic system is proposed to obtain the maximum available power. At first the performance ofconventional fuzzy logic controllers is compared with proposed method. Then the parameters of membership functionsof new mppt fuzzy logic controller are optimized by Genetic Algorithm. Simulation results indicate that the proposedmethod reduces the fluctuations near the optimal power point in comparison with conventional fuzzy logic controller.Also, the optimized proposed controller operates better than proposed controller which is not optimized.

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References


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