MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification
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.
[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.
[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.
@article{"International Journal of Electrical, Electronic and Communication Sciences:50496", author = "A. Chaouachi and R. M. Kamel and K. Nagasaka", title = "MPPT Operation for PV Grid-connected System using RBFNN and Fuzzy Classification", 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.", volume = "4", number = "5", pages = "773-9", }
