Open AccessFuzzy logic adaptive particle swarm optimisation based MPPT controller for photovoltaic systems
Author(s) -
Merchaoui Manel,
Hamouda Mahmoud,
Sakly Anis,
Mimouni Mohamed Faouzi
Publication year - 2020
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
eISSN - 1752-1424
pISSN - 1752-1416
DOI - 10.1049/iet-rpg.2019.1207
Subject(s) - maximum power point tracking , particle swarm optimization , photovoltaic system , control theory (sociology) , computer science , fuzzy logic , controller (irrigation) , maximum power principle , adaptive neuro fuzzy inference system , mathematical optimization , algorithm , fuzzy control system , power (physics) , mathematics , engineering , inverter , artificial intelligence , control (management) , agronomy , electrical engineering , biology , physics , quantum mechanics
Maximum power point tracking (MPPT) controllers are a key element in photovoltaic (PV) conversion systems since they allow extracting the maximum power from PV generators. Metaheuristic algorithms such as the particle swarm optimisation (PSO) are nowadays widely adopted and have shown their superiority to many other techniques. However, conventional PSO (CPSO) algorithms still suffer from the problem of long convergence time when the range of the search area is large. To overcome this issue, this study proposes a fast fuzzy logic PSO (FL‐PSO) based MPPT controller for PV systems. Unlike CPSO algorithm running with constant key parameters (inertia weight and acceleration coefficients), the proposed method includes a fuzzy inference system that dynamically adjusts these parameters. The effectiveness and rapidity of the proposed FL‐PSO algorithm is validated trough numerical simulations and experimental tests. The obtained results show the superiority of the proposal as compared to CPSO, Jaya and hill climbing algorithms even under partial shading conditions and abrupt change of solar irradiation.