Open AccessGenetically Optimization of an Asymmetrical Fuzzy Logic Based Photovoltaic Maximum Power Point Tracking Controller
Author(s) -
Ammar Al-Gizi,
Sarab Al-Chlaihawi,
Mohamed Louzazni,
Aurelian Crăciunescu
Publication year - 2017
Publication title -
advances in electrical and computer engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 23
eISSN - 1844-7600
pISSN - 1582-7445
DOI - 10.4316/aece.2017.04009
Subject(s) - photovoltaic system , fuzzy logic , control theory (sociology) , maximum power point tracking , computer science , controller (irrigation) , power (physics) , tracking (education) , point (geometry) , control engineering , artificial intelligence , engineering , mathematics , control (management) , electrical engineering , voltage , biology , inverter , physics , psychology , agronomy , geometry , quantum mechanics , pedagogy
This paper introduces a new fuzzy logic controller (FLC) based photovoltaic (PV) maximum power point tracking (MPPT) optimized with the genetic algorithm (GA). Four FLCs with five and seven numbers of triangular (tri) and generalized bell (g-bell) membership functions (MFs) are analyzed. The performances of the analyzed algorithms have been compared with the appropriate performances of the classical perturb and observe (P&O) algorithm by using the following criteria: the rise time (tr), the tracking accuracy of the output power, and the energy yield. The results showed that the FL-based PV MPPT controller with seven triangular (7-tri) MFs provides the best steady-state performances