{"title":"MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification","authors":"A. Chaouachi, R. M. Kamel, K. Nagasaka","volume":41,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":772,"pagesEnd":781,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/1357","abstract":"
This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW Photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three Radial Basis Function Neural Networks (RBFNN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated RBFNN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology, comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and non-linear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network.<\/p>\r\n","references":"[1] D. P. Hohm and M. E. Ropp \"Comparative Study of Maximum Power\r\nPoint Tracking Algorithms\" Progress in photovoltaics: research and\r\napplications, 2003, pp. 47-62.\r\n[2] V. Salas, E. Olias, A. Barrado, A. Lazaro, \"Review of the maximum\r\npower point tracking algorithms for stand-alone photovoltaic systems\",\r\nSolar Energy Materials & Solar Cells 90, 2006, pp. 1555-1578.\r\n[3] Trishan Esram, Patrick L. Chapman, \"Comparison of Photovoltaic Array\r\nMaximum Power Point Tracking Techniques\", Ieee Transactions On\r\nEnergy Conversion, Volume. 22, Number. 2, 2007, pp. 439-449.\r\n[4] Adel Mellit, and Soteris A. Kalogirou \"Artificial intelligence techniques\r\nfor photovoltaic applications: A review\", Progress in Energy and\r\nCombustion Science 34, 2008, pp. 574-632.\r\n[5] Theodore Amissah OCRAN, Cao Junyi, Cao Binggang, Sun Xinghua,\r\n\"Artificial Neural Network Maximum Power Point Tracker for Solar\r\nElectric Vehicle\" Tsinghua science and technology ISSN 1007-0214\r\n12\/23 Volume 10, Number 2, 2005, pp. 204-208.\r\n[6] A.B.G. Bahgat, N.H. Helwa, G.E. Ahmad, E.T. El Shenawy, \"Maximum\r\npower point tracking controller for PV systems using neural networks\",\r\nRenewable Energy 30, 2005, pp 1257-1268.\r\n[7] ZHANG Gao, FAN Ming, ZHAO Hongling, \u00d4\u00c7\u00ff\u00d4\u00c7\u00ffBagging Neural Networks\r\nfor Predicting Water Consumption,-- Journal of Communication and\r\nComputer, Volume 2, No.3 Serial No.4, 2005, pp: 19-24.\r\n[8] Xavier Anguera, Takahiro Shinozaki, Chuck Wooters and Javier\r\nHernando, \"Model Complexity Selection and Cross-Validation EM\r\nTraining for Robust Speaker Diarization\", in Proc. Of ICASSP, 2007.\r\n[9] Aymen Chaouachi, Rashad M. Kamel, Ken Nagasaka, \u00d4\u00c7\u00ff\u00d4\u00c7\u00ffModeling and\r\nSimulation of a Photovoltaic field Based on Matlab Simulink\",\r\nConference on energy system, economy and environment, Tokyo, 2009,\r\npp:70-74.\r\n[10] Gilbert M. Masters, \"Renewable and Efficient Electric Power Systems\".\r\nWiley Interscience, 2004.\r\n[11] R.M. Kamel, A.chaouachi, K.Nagasaka, \"Design and Implementation of\r\nVarious Inverter Controls to Interface Distributed Generators (DGs) in\r\nMicro Grids\", Conference on energy system, economy and environment,\r\nTokyo, 2009, pp: 60-64.\r\n[12] R. Lasseter, K. Tomsovic and P. Piagi, \"Scenarios for Distributed\r\nTechnology Applications with Steady State and Dynamic Models of\r\nLoads and Micro-Sources,\" CERTS Report, 2000.\r\n[13] L.I. Kuncheva, \"Fuzzy Classifier Design\", Physica-Verlag, Heidelberg,\r\n2000.\r\n[14] O. Cordon and F.H.M.J. Jesus, A proposal on reasoning methods in fuzzy\r\nrule-based classification systems. International Journal of Approximate\r\nReasoning. 20, 1999, pp. 21-45.\r\n[15] C.C. Lee , \"Fuzzy logic in control systems\", IEEE Transactions on\r\nSystems, Man & Cybernetics SMC-20 2, 1990, pp. 404-435.\r\n[16] Cornelius T.Leondes, Neural Network Systems Techniques and\r\nApplications, Volume 1 of Neural Network Systems architecture and\r\napplications, Academic Press, 1998.\r\n[17] E. J. Hartman, J. D. Keeler, and J. M. Kowalski, \u00d4\u00c7\u00ff\u00d4\u00c7\u00ffLayered neural\r\nnetworks with gaussian hidden units as universal approximators,-- Neural\r\nComput, 2:210-215, 1990.\r\n[18] Simon Haykin, Neural Networks. A Comprehensive Foundation, 2nd\r\nEdition, Prentice Hall, 1999.\r\n[19] Aymen Chaouachi, Rashad M. Kamel, Ken Nagasaka, \"Neural Network\r\nEnsemble-Based Solar Power Generation Short-Term Forecasting\",\r\nJournal of Advanced Computational Intelligence and Intelligent\r\nInformatics, Vol.14 No.1 Jan. 2010 pp. 69-75\r\n[20] Elisa Ricci, Renzo Perfetti, \"Improved pruning strategy for radial basis\r\nfunction networks with dynamic decay adjustment\", Neurocomputing,\r\npp: 1728-1732, 2006.","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 41, 2010"}