# MPPT-based fuzzy logic controller under partially shaded PV arrays and rapidly variation conditions

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2 346 includes the series resistance, A. Bouilouta et al. R s is depicted in Fig. 1b. [4] Fig. 1: a- An ideal solar cell and b- single diode model with The output current in Fig. 1b is I I PV I D. It can be written as: V IR I I I exp s PV 0 1 avt (1) where I PV is the current generated by the incidence of light, I 0 is the reverse saturation current, VT Ns kt is the thermal voltage of the PV module having N s e cells connected in series, q q is the electron charge, k is the Boltzmann constant, T is the temperature of the p n junction in K and a a is the diode ideality factor [4]. Eq. (1) does not adequately represent the behaviour of the cell when subjected to environmental variations, especially at low voltage. A more practical model can be seen in Fig. 2, where R s and R p represent the series and parallel resistances, respectively. An output current equation using this model can be written as [4]. V IRs V I R I I s PV I0 exp 1 a V T Rp (2) R s Fig. 2: Single diode model with R s and R p 3. FUZZY LOGIC ALGORITHM Fuzzy controller was based on fuzzy logic principle developed by Zadeh in 1965 [5]. This principle is basing on two inputs variables: the error E and a change in error E, and one output variable D (duty ratio variation). D value can be looked up in a rule base table such as Table 1 [2].

3 SIENR 2012: Diagnostic de défauts dans le système photovoltaïque par les 347 Fuzzy logic control generally consists of three stages: fuzzification, rule base table lookup, and defuzzification. During fuzzification, numerical input variables are converted into linguistic variables based on a membership function similar to Fig. 3. In this case, five fuzzy levels are used: NB (negative big), NS (negative small), ZE (zero), PS (positive small), and PB (positive big). In Fig. 3, a and b are based on the range of values of the numerical variable. Our simulation is based on genetic algorithm ( GA ) which chooses optimally and simultaneously both membership functions and control rules for the fuzzy logic controller as doing in [9]. Fig. 3: Membership functions for inputs and output of fuzzy logic controller The inputs to a MPPT fuzzy logic controller are usually an error E and a change in error E. The user has the flexibility of choosing how to compute E and E. Since d P dv vanishes at the MPP, [7, [10] uses the approximation P( n ) P( n 1) E( n ) (3) V( n ) V( n 1) and E( n) E( n) E( n 1) (4) Once E and E are calculated and converted to the linguistic variables, the fuzzy logic controller output, which is typically a change in duty ratio D of the power converter, can be looked up in a rule base table such as Table 1 [8]. Table 1: Fuzzy rule base table E NG NP EZ PP PG ΔE NG EZ EZ NG NG NG NP EZ EZ NP NP NP EZ NP EZ EZ EZ PP PP PP PP PP EZ EZ PG PG PG PG EZ EZ The linguistic variables assigned to D for the different combinations of E and E are based on the power converter being used and also on the knowledge of the user. Table 1 is based on a boost converter. If, for example, the operating point is far to the left of the MPP, that is E is PB, and E is ZE, then we want to largely increase the duty ratio, that is D should be PB to reach the MPP.

4 348 A. Bouilouta et al. 4. SIMULATION RESULTS To test the performance of fuzzy technique, a photovoltaic generator withfour modules was using, shown Fig. 4. Photovoltaic panel specifications are shown in Table 2. Fig. 4: Photovoltaic generator Table 2: PV module specialisations Designation BP SX150 Maximum power ( P max) 150 W Voltage at P max ( V PPM) 34.5 V Current at P max( I PPM ) 4.35 A Open-circuit voltage ( V oc ) 43.5 V Short-circuit current ( I cc ) 4.75 A The various parts of the fuzzy technique have been modelled using the Matlab/Simulink model as shown in Fig. 5. Fig. 5: Subsystem simulation of the fuzzy MPPT algorithm 4.1 Response for uniform and no uniform insolation during operation In the case where the first module receives irradiance of 550 W/m 2 and the others modules receive standard conditions( G 1000 W/ m and T 25C ), two peaks in power characteristic have been observed as shown in Fig. 6., with regard to Fig. 6 the global and local maximum power points are located at 353 W and 299 W, respectively. The produced output power of the fuzzy algorithm under uniform irradiation and no uniform irradiation (partial shading condition) are shown in Fig. 7. {a-;b-}, respectively. 2

6 350 A. Bouilouta et al. MPPs and seeks rapidly the global MPP without oscillations. It has been observed that the efficiency of the fuzzy algorithm depends to the type of load at which the generator is connected. Fig. 8:a- Irradiation variations Fig. 8:b- The generated output power of fuzzy algorithm REFERENCES [1] A. Zegaoui, M. Aillerie, P. Petit, J.P. Sawicki, J.P. Charles and A.W. Belarbi, Dynamic behaviour of PV Generator Trackers under Irradiation and Temperature Changes, Solar Energy, Vol. 85, N 11, pp , [2] C. Cabal, Optimisation Energétique de l Etage d Adaptation Electronique Dédié à la Conversion Photovoltaïque, Thèse de Doctorat, France Institute of Science, Toulouse, France, [3] T.Y. Kim, H.G. Ahn, S.K. Park and Y.K. Le, A Novel Maximum Power Point Tracking Control For Photovoltaic Power System Under Rapidly Changing Solar Radiation, IEEE International Symposium on Industry Electronics, ISIE, Vol. 2, pp , [4] K. Ishaquen, Z. Salam and H. Taheri, Simple, Fast and Accurate Two-Diode Model for Photovoltaic Modules, Solar Energy Materials and Solar Cells, Vol. 95, N 2, pp , [5] T.J. Ross, Fuzzy Logic with Engineering Applications, Second Edition, John Wiley and Sons Ltd., 628 p., [6] T. Esram and P.L. Chapman, Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques, IEEE Transactions on Energy Conversion, Vol. 22, N 2, [7] N. Khaehintung, K. Pramotung, B. Tuvirat and P. Sirisuk, RISC-Microcontroller Built-in Fuzzy Logic Controller of Maximum Power Point Tracking for Solar-Powered Light-Flasher Applications, In Proceedings IEEE Industrial Electronics Society, IECON 2004, Vol. 3, pp , [8] C.Y. Won, D.H. Kim, S.C. Kim, W.S. Kim and H.S. Kim, A New Maximum Power Point Tracker of Photovoltaic Arrays using Fuzzy Controller, 25 th Annual IEEE Power Electronics Specialists Conference, PESC 94, pp , 1994, [9] A. Messai, A. Mellit, A. Guessoum and S.A. Kalogirou, Maximum Power Point Tracking Using a GA Optimized Fuzzy Logic Controller And Its FPGA Implementation, Solar Energy, Vol. 85, N 2, pp , 2011.

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