Open AccessWind Turbine Maximum Power Point Tracking Using FLC Tuned with GA
Author(s) -
Haraoubia Mohamed Amine,
Abdelaziz Hamzaoui,
Najib Essounbouli
Publication year - 2014
Publication title -
energy procedia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.474
H-Index - 81
ISSN - 1876-6102
DOI - 10.1016/j.egypro.2014.12.398
Subject(s) - control theory (sociology) , maximum power point tracking , fuzzy logic , turbine , controller (irrigation) , maximum power principle , power (physics) , control engineering , point (geometry) , computer science , tracking (education) , wind power , genetic algorithm , energy (signal processing) , engineering , mathematics , control (management) , artificial intelligence , photovoltaic system , physics , inverter , electrical engineering , aerospace engineering , pedagogy , quantum mechanics , psychology , agronomy , biology , geometry , statistics , machine learning
The pursuit of the MPPT has led to the development of different kinds of controllers, one of which is the Fuzzy Logic Controller, which has proven it's worth. In order to further the advancement, Genetic Algorithms are introduced to give more precise settings to the controller. This work focuses on tuning the MFs of the fuzzy logic system by adapting their width in order to achieve a better result, thereby increasing the energy produced
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