{"title":"Maximum Power Point Tracking Using FLC Tuned with GA","authors":"Mohamed Amine Haraoubia, Abdelaziz Hamzaoui, Najib Essounbouli","volume":100,"journal":"International Journal of Electrical and Computer Engineering","pagesStart":434,"pagesEnd":438,"ISSN":"1307-6892","URL":"https:\/\/publications.waset.org\/pdf\/10000870","abstract":"
The pursuit of the MPPT has led to the development of many kinds of controllers, one of which is the Fuzzy Logic controller, which has proven its worth. To further tune this controller this paper will discuss and analyze the use of Genetic Algorithms to tune the Fuzzy Logic Controller. It will provide an introduction to both systems, and test their compatibility and performance.<\/p>\r\n","references":"[1]\tQuang, M. H. (2013). Optimisation de la production de l'\u00e9lectricit\u00e9renouvelable pour site isol\u00e9. Reims: University Reims.\r\n[2]\tBodenhofer, U. (1996). Tuning Of Fuzzy Systems.austria.\r\n[3]\tCAO.Y. J, Q. (1999). Teaching genetic algorithm using MATLAB. Manchester: Int. J. Elect. Enging. Educ.\r\n[4]\tJun Srisutapan, B. K. (2001). Fuzzy Logic and Genetic Algorithm for optimizing the approximate match of rules based on backpropagation neural networks. IEEE.\r\n[5]\tK.Rajangam, V. P. (2012). Fuzzy logic controlled genetic algorithm to solve the economic load dispatch for thermal power station.European Scientific Journal, 172-184.\r\n","publisher":"World Academy of Science, Engineering and Technology","index":"Open Science Index 100, 2015"}