Optimizing the operation of a photovoltaic generator by a genetically tuned fuzzy controller

This paper presents design and application of advanced control scheme which integrates fuzzy logic concepts and genetic algorithms to track the maximum power point in photovoltaic system. The parameters of adopted fuzzy logic controller are optimized using genetic algorithm with innovative tuning procedures. The synthesized genetic algorithm which optimizes fuzzy logic controller is implemented and tested to achieve a precise control of the maximum power point response of the photovoltaic generator. The performance of the adopted control strategy is examined through a series of simulation experiments which prove good tracking properties and fast response to changes of different meteorological conditions such as isolation or temperature.

[1]  Soteris A. Kalogirou,et al.  Maximum power point tracking using a GA optimized fuzzy logic controller and its FPGA implementation , 2011 .

[2]  P.S. Revankar,et al.  Maximum Power Point Tracking for PV Systems Using MATALAB/SIMULINK , 2010, 2010 Second International Conference on Machine Learning and Computing.

[3]  C. Larbes,et al.  Genetic algorithms optimized fuzzy logic control for the maximum power point tracking in photovoltaic system , 2009 .

[4]  P.L. Chapman,et al.  Comparison of Photovoltaic Array Maximum Power Point Tracking Techniques , 2007, IEEE Transactions on Energy Conversion.

[5]  Hyungsuck Cho,et al.  Genetic algorithm-based optimization of fuzzy logic controller using characteristic parameters , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

[6]  D. Graham,et al.  The synthesis of "optimum" transient response: Criteria and standard forms , 1953, Transactions of the American Institute of Electrical Engineers, Part II: Applications and Industry.

[7]  Hansruedi Bühler.  Réglage par logique floue , 1994 .

[8]  P. J. MacVicar-Whelan.  Fuzzy sets for man-machine interaction , 1976 .

[9]  P. Sirisuk,et al.  RISC-microcontroller built-in fuzzy logic controller of maximum power point tracking for solar-powered light-flasher applications , 2004, 30th Annual Conference of IEEE Industrial Electronics Society, 2004. IECON 2004.

[10]  C. Darwin.  The Origin of Species by Means of Natural Selection, Or, The Preservation of Favoured Races in the Struggle for Life , 2019 .

[11]  T. Muneer,et al.  Energy supply, its demand and security issues for developed and emerging economies , 2007 .

[12]  Angel Cid Pastor.  Conception et réalisation de modules photovoltaïques électroniques , 2006 .

[13]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[14]  Joseph Foran,et al.  Optimisation of a Fuzzy Logic Controller Using Genetic Algorithms , 2002 .

[15]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .