Journal of Renewable Energy and Sustainable Development (RESD) June 2015 - ISSN 2356-8569 11 RESD © 2015 http://apc.aast.edu Adaptive Artificial intelligence based fuzzy logic 1 Unité de Recherche Appliquée en Energies Renouvelables, URAER, Centre de Développement Des Energies Renouvelables, CDER, 47133, Ghardaïa, Algeria Layachi40@yahoo.fr I. INTRODUCTION In order to obtain an adequate output voltage, PV cells are connected in series to form a PV module. If higher voltages or currents are not available from a single module, modules must be connected into arrays. Series connections result in higher voltages, while parallel connections result in higher currents (Fig 1).The maximum power point tracking, MPPT technique, not only enables an increase in the power delivered from the PV module, but also enhances the operating lifetime of the PV system [2]. Various MPPT methods have been developed and implemented [3][4]. These methods can be differentiated based on various features including the types of sensors required, convergence speed, cost, range of effectiveness, implementation hardware requirements, and popularity [4]. MPPT techniques, such as the Perturb & Observe and the Fuzzy logic methods, will be compared using Matlab tool Simulink, considering different types of irradiation and temperature variations. The partially shaded condition will not be considered in the simulation: the irradiation is assumed to be uniformly spread over the PV array [4]. Fig 1. Example of PV arrays II. MODELLING AND CHARACTERISTIC OF PV ARRAY 1. Modeling of PV array A solar PV array is developed in Simulink. This array is used as a source for the maximum power point tracker system. The PV array makes use of the equations of a typical solar cell. The typical model of a solar cell is shown in Fig. 2. The current and voltage of the solar cell is given as follows [01]: (1) (2) Fig.2. Simplified equivalent circuit of solar cell where and are the cell output current and voltage. The definitions of the parameters are Keywords- Solar photovoltaic; MPPT; P&O; fuzzy logic Controller; PV array; DC/DC Buck-Boost converter Abstract— There is an increased need for analysing the effect of atmospheric variables on photovoltaic (PV) production and performance. The outputs from the different PV cells in different atmospheric conditions, such as irradiation and temperature, differ from each other, evidencing knowledge deficiency in PV systems. Maximum power point tracking (MPPT) methods are used to maximize the PV array output power by tracking continuously the maximum power point (MPP). Among all MPPT methods existing in the literature, perturb and observe (P&O) is the most commonly used for its simplicity and ease of implementation; however, it presents drawbacks, such as slow response speed, oscillation around the MPP in steady state, and even tracking in wrong way under rapidly changing atmospheric conditions. In order to allow a functioning around the optimal point Mopt, we have inserted a DC-DC converter (Buck– Boost) for a better matching between the PV and the load. In this paper, we study the Maximum power point tracking using adaptive Intelligent fuzzy logic and conventional (P&O) control for stand-alone photovoltaic Array system. In particular, the performances of the controllers are analyzed under varying weather conditions with constant temperature and variable irradiation. The proposed system is simulated by using MATLAB-SIMULINK. According to the results, fuzzy logic controller has shown better performance during the optimization. MPPTcontrol for stand-alone photovoltaic system under different atmospheric conditions 2 Electrical Engineering Department, University of Biskra, Algeria L. Zaghba 1,2 , N.Terki 2, A .Borni 1, A.Bouchakour 1 Journal of Renewable Energy and Sustainable Development (RESD) June 2015 - ISSN 2356-8569 12 RESD © 2015 http://apc.aast.edu given in table 1.The equivalent circuit for the solar cells arranged in parallel and series is shown in fig.3. Array current and array voltage become: (3) : represents the number of parallel modules. It should be noteded that each module is composed of cells connected in series. Corresponds to the short circuit current of the solar array. Fig.3. Electrically equivalent of solar array circuit ( Np parallel- Ns series) The output of Simulink model is shows first; the V-P characteristics of PV module, for various irradiation levels (Fig.7), and then V-I characteristics, reference to the key specifications of the MSX60 array are illustrated in table 2 [01]. The results of Simulink PV module show the excellent correspondence to the model. Table 1. Electrical specifications of the -60 W mono-crystalline photovoltaic module MSX60 Parameter Value Maximum Power PPV 200W Tension at Pmax VMPP 26.3 V Current at Pmax IMPP 7.61A Open Circuit Voltage Voc 32.9V Short Circuit Current Isc 8.21A Ideality factor A 1.3 Table 2. Electrical specifications of the - 6KW mono-crystalline photovoltaic array of 100 module of MSX60 Parameter Value Maximum Power PPV 60X100 = 6000W Tension at Pmax VMPP 17.1X20 = 342 V Current at Pmax IMPP 3.5X5 = 17.5A Open Circuit Voltage Voc 21.1 X20 = 422V Short Circuit Current Isc 3.8X5 = 19 A 0 50 100 150 200 250 300 350 0 5 10 15 20 25 30 Voltage (V) C u rr e n t (A ) T = 0 C° T = 25 C° T = 50 C° T = 75 C° T = 100 C° G=1000 W/m² T =25 C° ,G=1000 W/m² Pv Array = 6 KW Fig .4. V-I, Characteristics of PV Array (6KW) at constant insulations and varying temperature 0 50 100 150 200 250 300 350 0 1000 2000 3000 4000 5000 6000 7000 Voltage (V) P o w e r (W ) T =0 C° T =25 C° T =50 C° T =75 C° T =100 C°G = 1000 W/m² T =25 C° ,G=1000 W/m² Pv Array = 6 KW Fig.5.P-V Characteristics of PV Array (6KW) at constant insulations and varying temperature. 0 50 100 150 200 250 300 350 0 10 20 30 40 Voltage (V) C u rr e n t (A ) G=200 W/m² G=400 W/m² G=600 W/m² G=800 W/m² G=1000 W/m² T =25 C° ,G=1000 W/m² Pv Array = 6 KW T =25 C° Fig .6. V-I Characteristics of PV Array (6KW) at constant temperature and varying insulations 0 100 200 300 400 0 1000 2000 3000 4000 5000 6000 7000 Voltage (V) P o w er ( W ) G=200 W /m² G=400 W /m² G=600 W /m² G=800 W /m² G=1000 W /m² T = 25 C° at G=1000 W/m² and T = 25 C° Pv array = 6KW Fig .7. V-P Characteristics of PV Array (6KW) at constant temperature and varying insulations 2. DC-DC Buck-Boost Converter The DC-DC converter is an electronics circuit, which is used to provide a loss less transfer of energy between different circuits at different DC voltage levels. There are many DC-DC converters. One of the popular types of DC-DC converters is buck- boost converter. The Buck-boost converter is used to step down and step up the DC voltage by changing the duty ratio of the MOSFET. If the duty ratio is less than 0.5, the output voltage is less than the input voltage; however, if the duty ratio is greater than 0.5, the output voltage will be greater than the input voltage. Duty ratio is the time at which the MOSFET is on to the total switching time. The buck-boost converter is shown in Figure 8.The relation between the input and the output voltages of the buck-boost converter is given as follows: [7]. (4) Journal of Renewable Energy and Sustainable Development (RESD) June 2015 - ISSN 2356-8569 13 RESD © 2015 http://apc.aast.edu Table 3. Buck-boost converter parameters Buck-boost converter parameters L 1mH C1 1000 µF C2 330 µF fs 40KHZ Resistive Load R 5Ω When applying Kirchhoff's laws, we find:             R V iD dt dV C VDVD dt di L D C i C i dt dV PV PVPV PVPV )1( .).1( (5) I is the current through the inductance; V is the voltage across the capacitor; D is the duty ratio and Vpv is the voltage measured from the photovoltaic panel Fig 8. Fig. 8. The buck-Boost converter circuit Fig. 9. Block diagram of PV Module with MPPT Controller A. MPPT using Perturbation & Observe This technique introduces a slight perturbation by decreasing or increasing the PWM duty cycle of the Buck converter. This perturbation changes the power of the solar module. If the power increases due to the perturbation, the perturbation is continues in that direction [06]. After the peak power is reached, the power at the next instant decreases and hence that the perturbation reverses. When the steady state is reached, the algorithm oscillates around the peak point. To keep the power variation small, the perturbation size is kept very small. The flow chart of algorithm has 4 cases as shown in Fig.10 [06]. Fig .10. Configuration of Fuzzy Logic Controller in matlab/simulink B. MPPT using Fuzzy Logic Control Fuzzy logic controllers have been introduced recently in 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 complete knowledge of the exact model and can handle nonlinearity. The proposed fuzzy logic MPPT Controller, shown in Figure 11, has two inputs and one output. The two input variables are the error E and change of error CE at sampled times k defined by eq. 6 and 7, where P and V are the PV panel power and voltage respectively at instant k: [8][9] [10][11] (6) (7) Where: and are the power and the voltage of the PV generator respectively at instant k. The power of the PV system: Start Mesure V(i),I(i) P(i) = V(i)*I(i) P> 0 V(i)V(i- 1) D(i) = D(i-1)- D D(i) = D(i-1)- D D(i) = D(i-1)- D D(i) = D(i-1)- D Update V(i-1) = V(i) ;I(i-1) =I(i) Retur n 3. Maximum Power Point Tracking Maximum Power Point tracking controller is basically used to operate the Photovoltaic modules in manner that allows the load connected with the PV module to extract the maximum power, which the PV module is capable to produce at given atmospheric conditions. PV cells have a single operating point, where the value of the current and voltage of the cell results in a maximum power output. With the varying atmospheric condition and because of the rotation of the earth [4], the irradiation and temperature keeps on changing throughout the day. So it is a big challenge to operate a PV module consistently on the maximum power point and for which many MPPT algorithms have been developed [1]. The most popular among the available MPPT techniques is Perturb and Observe (P&O) method. This method is having its own merits and demerits. The aim of the present work is to develop the Simulink model of P&O MPPT controller and then the fuzzy intelligent control has introduced on it to improve its overall performance Journal of Renewable Energy and Sustainable Development (RESD) June 2015 - ISSN 2356-8569 14 RESD © 2015 http://apc.aast.edu (8) The input E(k) shows the following: the operation point at the instant k is located on the right or on the left of the MPP on the PV characteristic curve as shown in figure 12, while the input CE(k) shows moving the direction of this point. Where the control action D is duty cycle of PWM signal that control the Buck Boost converter [5] [6][7][8]. Fig.11. Block diagram of the fuzzy controller The fuzzy controller design contains the three following steps:  Fuzzification The fuzzification is the process of converting the system actual inputs values E and CE into linguistic fuzzy sets using fuzzy membership function. These variables are expressed in terms of five linguistic variables (such as ZE(zero), PB (positive big), PS (positive small), NB (negative big), NS (negative small)), using basic fuzzy sub sets as shown in Fig.13  Rule base & inference engine Fuzzy rule base is a collection of if-then rules that contain all the information for the controlled parameters. It is set according to professional experience and the operation of the system control. The fuzzy rule algorithm includes 25 fuzzy control rules listed in table 3 [5] [6][7][8]. Fuzzy inference engine is an operating method that formulates a logical decision, based on the fuzzy rule setting and transforms the fuzzy rule base into fuzzy linguistic output. In this paper, Mamdani’s fuzzy inference method, with Max-Min operation fuzzy combination, has been used [9][10][11]. (b) (c) Fig.12. Membership function of E, CE and D  Defuzzification Defuzzification of the inference engine evaluates the rules, based on a set of control actions, for a given fuzzy inputs set. This operation converts the inferred fuzzy control action into a numerical value at the output by forming the union of the outputs resulting from each rule. The center of area (COA) algorithm is used for defuzzification of output duty control parameter, i.e. If E is NB and CE is ZO, then crisp D is PB. This means that if the operating point is far away from the MPP by the right side, and the variation of the slope of the curve is almost Zero, this will increase the duty cycle. The Output of duty cycle D is expressed by [10][11][12][13]: (9) Table 4. Fuzzy Rules Table E/CE NG NP ZE PP PG NG ZE ZE PG PG PG NP ZE ZE PP ZE PP ZE PP ZE ZE ZE NP PP NP NP NP ZE ZE PG NG NG NG ZE ZE Fig. 13.The input-output surface waveform of the FLC Inferen ce Fuzzificati on Deffuzificati on Rules E CE D (a) Journal of Renewable Energy and Sustainable Development (RESD) June 2015 - ISSN 2356-8569 15 RESD © 2015 http://apc.aast.edu III. SIMULINK MODEL OF PV SYSTEM WITH P&O AND FUZZY LOGIC CONTROLLER The performance of the tow systems, namely perturb &observe (P&O) and fuzzy logic controller, are analyzed. The performances of the controllers are analyzed in the following conditions: Constant temperature and variable irradiation Fig 14.Simulation Block Diagram of MPPT PV systems for Maximum using P&O and Fuzzy Logic Controller A. Operation under Constant Conditions In this case, the temperature and irradiation are considered constant. the values are taken under standard conditions: temperature25°Cand irradiation in 1000W/m2. B. Operation with Variable Conditions In this case the temperature and irradiation are changing with time under different weather condition. Fig. 9 shows how the irradiance is changing for the PV solar panel. The voltage and the current vary depending on irradiance. The curve of variable irradiance is plotted using a signal builder, where the irradiance is not very realistic, because these are instantaneous changing irradiances. The simulation results are shown in the next figures. : Fig.15. Variation of irradiance used in simulation. C. P&O Mppt Controller Fig.16. Input and Output Current of the Buck Boost converter with P&O Mppt Controller at constant temperature (T=25 C°) and varying insulation Fig.17. Input and Output Voltage of the Buck Boost converter with P&O Mppt Controller at constant temperature (T=25 C°) and varying insulation 0 0.2 0.4 0.6 0.8 1 -4000 -2000 0 2000 4000 6000 8000 Time(s) P o w e r (W ) Output of the Pv array Output of the Buck Boost Converter 600 W/m² 800 W/m² 300 W/m² 1000 W/m² Fig.18. Input and Output Power of the Buck Boost with P&O Mppt Controller at constant temperature (T=25 C°) and varying insulation D. Fuzzy Logic Mppt Controller 0 0.2 0.4 0.6 0.8 1 0 10 20 30 40 50 60 70 time (s) C u rr e n t (A ) Output of the Buck Boost Converter Output of the Pv array 600 W/m² 300 W/m² 1000 W/m² 800 W/m² Fig.19. Input and Output Current of the Buck Boost converter with fuzzy logic Mppt Controller at constant temperature (T=25 C°) and varying insulation Pv Array Continuous powergu Scope Scope Scope Scope Saturatio Product Product Product Vi Insolatio Tem Vou Ip I Ip D Vp P_p Ipv PV Voltage I V D P & O Output Voltage Outout Power Manual P V D MPPT Fuzzy Logic mod a fc U ML Control Input Voltage 2 Constant2 E D i Ic Vc BUCK_BOOST NON_INVERSE Ad 0 0.2 0.4 0.6 0.8 1 -200 -100 0 100 200 300 400 500 Time (s) V o lt a g e ( V ) Output of the Pv array Output of the Buck Boost Converter 300 W/m² 800 W/m² 600 W/m² 1000 W/m² 0 0.2 0.4 0.6 0.8 1 0 5 10 15 20 25 30 35 40 time (s) C u rr e n t (A ) Output of the Pv Array Output of the Buck Boost Converter 600 W/m² 800 W/m² 300 W/m² 1000 W/m² Journal of Renewable Energy and Sustainable Development (RESD) June 2015 - ISSN 2356-8569 16 RESD © 2015 http://apc.aast.edu 0 0.2 0.4 0.6 0.8 1 -100 0 100 200 300 400 500 Time (s) V o lt a g e ( V ) Output of the Buck Boost Convertert Output of the of the PV array 1000 W/m² 600 W/m² 300 W/m² 800 W/m² Fig.20. Input and Output Voltage of the Buck Boost with fuzzy logic Mppt Controller at constant temperature (T=25 C°) and varying insulation 0 0.2 0.4 0.6 0.8 1 -2000 0 2000 4000 6000 8000 10000 time (s) P o w e r (W ) Output of the PV array Output of the Buck Boost Converter 1000 W/m² 600 W/m² 300 W/m² 800 W/m² Fig.21. Input and Output Power of the Buck Boost converter with fuzzy logic Mppt Controller at constant temperature (T=25 C°) and varying insulation As shown, fuzzy controller gives smother power signal line, less oscillation and better stable operating point than P&O. From the simulation results, it can be deduced that the fuzzy controller gives better performance than P&O, and it has more accuracy for operating at Maximum Power Point. IV. CONCLUSION This paper presents the performance of tow MPPT algorithms for tracking the maximum power available in PV array system, with Fuzzy Logic controller and P&O. The algorithms works as a direct method of MPPT through a buck-boost converter placed in parallel with the PV array. Based on the simulation results with MATLAB/SIMULINK, it can be observed that all of the tow MPPT controllers can be used to track the MPP under variable changes of solar irradiance and cell temperature. The tow controllers regulate the PV array voltage to operate at MPP operating voltage in order to produce the maximum power. However, it can be concluded that fuzzy logic has a better steady state, less oscillation around the MPP and dynamical performance than traditional P&O. REFERENCES [1] Aurobinda Panda,.Pathak, M.K., and Srivastava, S.P.« Fuzzy Intelligent Controller for the Maximum Power Point Tracking of a Photovoltaic Module at Varying Atmospheric Conditions ». Journal of Energy Technologies and Policy. Vol.1, no.2. pp.18-27, 2011. 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