Fuzzy logic adaptive particle swarm optimisation based MPPT controller for photovoltaic systems
| Autor: | Manel Merchaoui, Mohamed Faouzi Mimouni, Mahmoud Hamouda, Anis Sakly |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Maximum power principle
Renewable Energy Sustainability and the Environment Computer science 020209 energy 020208 electrical & electronic engineering Photovoltaic system MathematicsofComputing_NUMERICALANALYSIS Particle swarm optimization 02 engineering and technology Fuzzy logic Maximum power point tracking Control theory Convergence (routing) 0202 electrical engineering electronic engineering information engineering Key (cryptography) Hill climbing |
| Zdroj: | IET Renewable Power Generation. 14:2933-2945 |
| ISSN: | 1752-1424 1752-1416 |
| DOI: | 10.1049/iet-rpg.2019.1207 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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