MAXIMUM POWER POINT TRAKING TECHNICAL BASED ON FUZZY LOGIC CONTROLLER FOR PHOTOVOLTAIC SYSTEM

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1 MAXIMUM POWER POINT TRAKING TECHNICAL BASED ON FUZZY LOGIC CONTROLLER FOR PHOTOVOLTAIC SYSTEM 1 BOUTHAINA MADACI, 2 KAMEL EDDINE HEMSAS, 3 RACHID CHENNI, 4 ABDELGHANI KHELLAF 1,3Mentouri University Constantine, 2,4University Sétif 1, Algeria 1bmadaci964@gmail.com, 2hemsas.kamel@gmail.com, 3chenni.rachid@yahoo.fr, 4khellafabdelghani3@gmail.com Abstract: This paper presents an intelligent method of maximum power point tracking (MPPT) technique using fuzzy logic controller (FLC) for photovoltaic system, which consists of the PV panel coupled with dc-dc buckboost controlled by a MPPT algorithm techniques, permits to feed a storage battery. The proposed algorithms will be simulated in Matlab/Simulink environment. The PV system has been tested with KC200GT solar panel under various operating conditions with and without MPPT technique. Index Terms: Photovoltaic;Fuzzy Logic Controller, battery. I. INTRODUCTION The renewable energy sources such as solar, wind, biomass etc, are important branch of electricity generation. The energy solar is one of the most promising renewable energy resources [1], it almost free, easy maintenance, environmental friendly [2]- [5]. The quantity of electric power generated by solar PV changes continuously with weather conditions (isolation and temperature) [6] - [7]. Accordingly, PV module has nonlinear characteristics source that depends on irradiation and temperature in its operation. Maximum power tacking controller is required to extract maximum power from the PV system by controlling the dc-dc converter [8]-[9]. There are many MPPT techniques such as perturb and observe method, incremental conductance method, and intelligent methods (Neural Network, Fuzzy Logic Controller) etc.. These methods vary in their complexity, different topologies and hardware implementation [10]. In order to improve energy conversion efficiency of the photovoltaic generator, this paper presents an intelligent method of maximum power point tracking (MPPT) technique using fuzzy logic controller (FLC), under various climatic conditions. The paper is organized as follows: Section 2 discusses mathematical modeling of PV system. In section 3 the proposed MPPT techniques are described, in section 4 simulation results in the Matlab/Simulink are presented. Finally, conclusions are presented in section 5. Figure 1: Schematic of the PV system. Modeling of PV panel In this work, we have chosen the model a diode which is the most classic and most used. The model of the equivalent circuit of a PV cell is shown in Fig. 2. Figure 2: Equivalent schema of a PV cell. The output current from the PV cell is given by relation (1) [11]-[12], II. PV SYSTEM MODELING Fig.1 shown the configuration of PV system, consist of the PV panel, buck-boost converter placed between the PV panel and the load, the duty cycle of the buckboost converter is controlled by using MPPT algorithm techniques. 7

2 Figure 3:I V and P V Characteristics of a PV module under STC conditions. 3) Battery model A simple equivalent circuit battery model of the type lead acid is show in Fig. 4.The model includes a voltage source V1in series with an internal resistance R1 [14], Figure 4: Equivalent circuit of battery Current voltage (I-V) and power voltage (P-V) characteristics of a PV module under STC conditions are shown in Fig.3 (a) and Fig.3 (b) respectively. 8

3 III. FUZZY LOGIC CONTROL FOR MPPT A block diagram of the fuzzy logic controller (FLC) for MPPT is shown in Fig. 5 [15], Figure 6: Membership function for inputs and output of fuzzy logic controller. Five fuzzy levels for input/output are used: NB negative big, NS negative small, ZE zero, PS positive small, and PB positive big [16]- [18]. The Table 2 shows the rules set for the five level fuzzy logic controller, which consists of 25 rules [12]. Figure 5: Block diagram of fuzzy logic controller Where the error E and change in error CE are the inputs variables to a MPPT FLC and a change in the duty cycle of the dc-dc converter ΔD is the output of the FLC. E and CEare defined asfollows: IV. RESULT AND DESCUSSION The simulation of the PV system consists of photovoltaic panel, buck-boost converter, battery as shown in Fig. 7. The Model of the fuzzy logic controller MPPT under Matlab/Simulink is shows in Fig 8. Figure 7: Model of the PV system under Matlab/Simulink Figure 8: Model of the fuzzy logic MPPT undermatlab/simulink. In this section simulation results of the PV system have be tested under different irradiation and temperature: 9

4 1) Effects of solar irradiation changes The solar irradiation is changes, its value as follows: 1000W/m2 from the time 0< t < 4s, 800W/m2 from the time 4< t <6 s, 600 W/m2 from the time 6 < t < 8 s and 1000W/m2 from the time 8< t < 10s, meanwhile the temperature is kept constant at 25 C. Fig.9, fig.10 and fig.11 show the output power (Ppv), output voltage (VPV) of the PV system and state of charge (SOC) of the battery with and without MPPT.The fuzzy logic controller builds the control signal duty cycle (D) of the buck-boost converter, as show in fig.12. Figure 12: Duty cycle (D) of the buck-boost converter under varying irradiance conditions. Figure 9: Output power of the PV system under varying irradiance conditions with and without MPPT. 2) Impacts of temperature variations The irradiation constant (1000W/m2), when the temperature is changes, its value as follows: 75 C from the time 0< t < 3s, 25W C from the time 3< t <6 s, 50 C from the time 6 < t < 9 s and 25 C from the time 9< t < 10s. Fig.13, fig.14 and fig.15 show the output power (Ppv), output voltage (VPV) of the PV system and state of charge (SOC) of the battery with MPPT and without MPPT.The fuzzy logic controller builds the control signal duty cycle (D) of the buckboost converter, as show in fig.16. Figure 10: Output voltage (Vpv) of the PV system under varying irradiance conditions with and without MPPT. Figure 13: Output power of the PV system under varying temperature conditions with and without MPPT Figure 11: State of charge (SOC) of battery under varying irradiance conditions with and without MPPT. Figure 14: Output voltage (Vpv) of the PV system under varying temperature conditions with and without MPPT 10

5 Figure 15: State of charge (SOC) of the battery under varying temperature conditions with and without MPPT. Figure 16: Duty cycle (D) of the buck-boost converter under varying temperature conditions. From the simulation results, it can be deduced that the output power without fuzzy logic controller MPPT under varying operating conditions (irradiance and temperature) shown in Fig.9 and Fig. 13 respectively. It is not the actual Maximum Power Point (Pmpp). However, in the same atmospheric condition, the output power with fuzzy logic controller MPPT is 200 W, W and W, extremely corresponding to the MPPs; it was capable to maintain the voltage array on its MPP operation. CONCLUSION The work presented in this paper consists of a study, simulation of the PV system with MPPT techniques which is: fuzzy logic controllers (FLC). Simulation results show that FLC MPPT was capable to track the MPP with quicker response, a less oscillating when the MPP is reached in case of sudden change in irradiance and temperature. REFERENCES 1. M. A. A. M. Zainuri, M. A. M. Radzi, A. C. Soh and N. A. Rahim, Development of adaptive perturb and observefuzzy control maximum power point tracking forphotovoltaic boost dc dc converter, IET Renewable Power Generation, M. A. Eltawil, Z. Zhao, MPPT techniques for photovoltaic applications, Renewable and Sustainable Energy Reviews, p , C.Chiu and Y. Ouyang, Robust Maximum Power Tracking Control of Uncertain Photovoltaic Systems: A UnifiedT-S Fuzzy Model-Based Approach, IEEE Transactions On Control Systems Technology, Vol. 19, NO. 6, November B. Subudhi and R. Pradhan, A Comparative Study on Maximum Power Point Tracking Techniques for Photovoltaic Power Systems, IEEE Transaction sonsustainable energy, Vol.4,No 1,January C. Chiu, T-S Fuzzy Maximum Power Point Tracking Control of Solar Power Generation Systems,, IEEE Transactions On Energy Conversion, Vol. 25, No. 4, December R. F. Bastos, C. R. Aguiar, A. F. Q. Gonçalves, and R. Q. Machado, An Intelligent Control System Used to Improve Energy Production From Alternative Sources With DC/DC Integration, IEEE Transactions On Smart Grid, Vol. 5, No. 5, September S. Dhar, R Sridhar, and G.Mathew, Implementation of PV Cell Based Standalone Solar Power System Employing Incremental Conductance MPPT Algorithm, International Conference on Circuits, Power and Computing Technologies [ICCPCT], S. Charfi,M. Chaabene, A comparative study of MPPT techniques for PV systems, The fifth International Renewable Energy Congress IREC, March 25 27, Hammamet, Tunisia, R. Rahmani, M. Seyedmahmoudian, S. Mekhilef and R. Yusof, Implementation of fuzzy logic Maximum power point tracking Controller for photovoltaic system, American Journal of Applied Sciences, 10 (3): , B. Madaci, R. Chenni, E. Kurt, K. E.Hemsas, Design and control of a stand-alone hybrid power system, International Journal of Hydrogen Energy, p , M.G.Villavla, J.R.Gazoli, E.R.Filho, Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays, IEEE transactions on power electronics, Vol. 24, No. 5, MAY B. Madaci, R. Chenni, E. Kurt, K. E.Hemsas, Comparison of two maximum power point tracking techniques applied to a photovoltaic system, Third European Conference on Renewable Energy Systems (ECRES), A.H.M Nordin, A.M.Omar, Modeling and simulation of photovoltaic (PV) Array and Maximum Power Point Traker (MPPT) for Grid-connected PV System, Malaysia, M. Rebhi, A. Benatillah, M. Sellam, B. Kadri, Comparative Study of MPPT Controllers for PV System Implemented in the South-west of Algeria, Energy Procedia, p , M. Salhi, A. El-Jouni, R. El-Bachtiri, Maximum Power Point Tracker using Fuzzy Control for Photovoltaic System, International Journal of Emerging Research in Management &Technology, T. Esram and P. L. Chapman, Comparison of Photovoltaic Array M aximum PowerPoint Tracking Techniques, IEEE Transactions On Energy Conversion, Vol. 22, No. 2, June R. EL GOURI, M. B. Brahim, L. Hlou, A comparative study of mppt technical based on fuzzy logic and perturb observe algorithms for photovoltaic systems, Journal of Theoretical and Applied Information Technology, Vol. 58 No.2, 20 th December Ch. Kiran Kumar, T. Dinesh, S.Ganesh Babu, Design and Modelling of PV system and different MPPT algorithms, International Journal of Engineering Trends and Technology (IJETT) Volume 4 Issue 9- Sep