MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification

Authors: A. Chaouachi, R. M. Kamel, K. Nagasaka

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.

Keywords: MPPT, neuro-fuzzy, RBFN, grid-connected, photovoltaic.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1055046

References:

[1] D. P. Hohm and M. E. Ropp "Comparative Study of Maximum Power Point Tracking Algorithms" Progress in photovoltaics: research and applications, 2003, pp. 47-62.
[2] V. Salas, E. Olias, A. Barrado, A. Lazaro, "Review of the maximum power point tracking algorithms for stand-alone photovoltaic systems", Solar Energy Materials & Solar Cells 90, 2006, pp. 1555-1578.
[3] Trishan Esram, Patrick L. Chapman, "Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques", Ieee Transactions On Energy Conversion, Volume. 22, Number. 2, 2007, pp. 439-449.
[4] Adel Mellit, and Soteris A. Kalogirou "Artificial intelligence techniques for photovoltaic applications: A review", Progress in Energy and Combustion Science 34, 2008, pp. 574-632.
[5] Theodore Amissah OCRAN, Cao Junyi, Cao Binggang, Sun Xinghua, "Artificial Neural Network Maximum Power Point Tracker for Solar Electric Vehicle" Tsinghua science and technology ISSN 1007-0214 12/23 Volume 10, Number 2, 2005, pp. 204-208.
[6] A.B.G. Bahgat, N.H. Helwa, G.E. Ahmad, E.T. El Shenawy, "Maximum power point tracking controller for PV systems using neural networks", Renewable Energy 30, 2005, pp 1257-1268.
[7] ZHANG Gao, FAN Ming, ZHAO Hongling, ÔÇÿÔÇÿBagging Neural Networks for Predicting Water Consumption,-- Journal of Communication and Computer, Volume 2, No.3 Serial No.4, 2005, pp: 19-24.
[8] Xavier Anguera, Takahiro Shinozaki, Chuck Wooters and Javier Hernando, "Model Complexity Selection and Cross-Validation EM Training for Robust Speaker Diarization", in Proc. Of ICASSP, 2007.
[9] Aymen Chaouachi, Rashad M. Kamel, Ken Nagasaka, ÔÇÿÔÇÿModeling and Simulation of a Photovoltaic field Based on Matlab Simulink", Conference on energy system, economy and environment, Tokyo, 2009, pp:70-74.
[10] Gilbert M. Masters, "Renewable and Efficient Electric Power Systems". Wiley Interscience, 2004.
[11] R.M. Kamel, A.chaouachi, K.Nagasaka, "Design and Implementation of Various Inverter Controls to Interface Distributed Generators (DGs) in Micro Grids", Conference on energy system, economy and environment, Tokyo, 2009, pp: 60-64.
[12] R. Lasseter, K. Tomsovic and P. Piagi, "Scenarios for Distributed Technology Applications with Steady State and Dynamic Models of Loads and Micro-Sources," CERTS Report, 2000.
[13] L.I. Kuncheva, "Fuzzy Classifier Design", Physica-Verlag, Heidelberg, 2000.
[14] O. Cordon and F.H.M.J. Jesus, A proposal on reasoning methods in fuzzy rule-based classification systems. International Journal of Approximate Reasoning. 20, 1999, pp. 21-45.
[15] C.C. Lee , "Fuzzy logic in control systems", IEEE Transactions on Systems, Man & Cybernetics SMC-20 2, 1990, pp. 404-435.
[16] Cornelius T.Leondes, Neural Network Systems Techniques and Applications, Volume 1 of Neural Network Systems architecture and applications, Academic Press, 1998.
[17] E. J. Hartman, J. D. Keeler, and J. M. Kowalski, ÔÇÿÔÇÿLayered neural networks with gaussian hidden units as universal approximators,-- Neural Comput, 2:210-215, 1990.
[18] Simon Haykin, Neural Networks. A Comprehensive Foundation, 2nd Edition, Prentice Hall, 1999.
[19] Aymen Chaouachi, Rashad M. Kamel, Ken Nagasaka, "Neural Network Ensemble-Based Solar Power Generation Short-Term Forecasting", Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.14 No.1 Jan. 2010 pp. 69-75
[20] Elisa Ricci, Renzo Perfetti, "Improved pruning strategy for radial basis function networks with dynamic decay adjustment", Neurocomputing, pp: 1728-1732, 2006.