Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems


The employment of maximum power point tracking techniques in the photovoltaic power systems is well known and even of immense importance. There are various techniques to track the maximum power point reported in several literatures. In such context, there is an increasing interest in developing a more appropriate and effective maximum power point tracking control methodology to ensure that the photovoltaic arrays guarantee as much of their available output power as possible to the load for any temperature and solar radiation levels. In this paper, theoretical details of the work, carried out to develop and implement a maximum power point tracking controller using neural networks for a stand-alone photovoltaic system, are presented. Attention has been also paid to the command of the power converter to achieve maximum power point tracking. Simulations results, using Matlab/Simulink software, presented for this approach under rapid variation of insolation and temperature conditions, confirm the effectiveness of the proposed method both in terms of efficiency and fast response time. Negligible oscillations around the maximum power point and easy implementation are the main advantages of the proposed maximum power point tracking (MPPT) control method.

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Essefi, R. , Souissi, M. and Abdallah, H. (2014) Maximum Power Point Tracking Control Using Neural Networks for Stand-Alone Photovoltaic Systems. International Journal of Modern Nonlinear Theory and Application, 3, 53-65. doi: 10.4236/ijmnta.2014.33008.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Yang, D. and Dylan Dah-Chuan, L. (2011) Battery-Integrated Boost Converter Utilizing Distributed MPPT Configuration for Photovoltaic Systems. Solar Energy, 85, 1992-2002.
[2] Heydari-Doostabad, H., Keypour, R., Khalghani, M.R. and Khooban, M.H. (2013) A New Approach in MPPT for Photovoltaic Array Based on Extremum Seeking Control under Uniform and Non-Uniform Irradiances. Solar Energy, 94, 28-36.
[3] Chaouachi, A., Kamel, R.M. and Nagasaka, K. (2010) A Novel Multi-Model Neuro-Fuzzy-Based MPPT for Three-Phase Grid-Connected Photovoltaic System. Solar Energy, 84, 2219-2229.
[4] Mahjoub-Essefi, R., Souissi, M. and Hadj-Abdallah, H. (2014) Maximum Power Point Tracking Control Technique for Photovoltaic Systems Using Neural Networks. Proceedings of the 5th Annual International Renewable Energy Congress IREC’2014, Hammamet, 25-27 March 2014, 422-427.
[5] Ben-Saleh, C. and Ouali, M. (2011) Comparison of Fuzzy Logic and Neural Network in Maximum Power Point Tracker for PV Systems. Electric Power Systems Research, 81, 43-50.
[6] Morales, D.S. (2010) Maximum Power Point Tracking Algorithms for Photovoltaic Applications. Master’s Thesis, Aalto University, Espoo and Helsinki.
[7] Liu, Y., Liu, C., Huang, J. and Chen, J. (2013) Neural-Network-Based Maximum Power Point Tracking Methods for Photovoltaic Systems Operating under Fast Changing Environments. Solar Energy, 89, 42-53.
[8] Gao, X.W., Li, S.W. and Gong, R.F. (2013) Maximum Power Point Tracking Control Strategies with Variable Weather Parameters for Photovoltaic Generation Systems. Solar Energy, 93, 357-367.
[9] Bouilouta, A., Mellit, A. and Kalogirou, S.A. (2013) New MPPT Method for Stand-Alone Photovoltaic Systems Operating under Partially Shaded Conditions. Energy, 55, 1972-1185.
[10] Almonacid, F., Fernandez, E.F., Rodrigo, P., Pérez-Higueras, P.J. and Rus-Cascas, C. (2013) Estimating the Maximum Power of a High Concentrator Photovoltaic (HCPV) Module Using an Artificial Neural Network. Energy, 53, 165-172.
[11] Hudson Beale, M., T. Hagan, M. and B. Demuth, H. (2013) Neural Network ToolboxTM: User’s Guide. Matlab-MathWorks.
[12] Chiung-Chou, L. (2010) Genetik k-Means Algorithm Based Network for Photovoltaic MPP Prediction. Energy, 35, 529-536.
[13] Cimbala, J.M. (2011) Basic Statistics Course.
[14] Liu, Y. and Huang, J. (2011) A Fast and Low Cost Analog Maximum Power Point Tracking Method for Low Power Photovoltaic Systems. Solar Energy, 85, 2771-2780.

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