Fuzzy logic controller based maximum power point tracking and its optimal tuning in photovoltaic systems
Conventionally, the parameters of a fuzzy logic controller (FLC) are obtained by a trial and error method or by human experience. In this paper, the problem of designing a FLC for maximum power point tracking (MPPT) of a photovoltaic system (PV) that consists of a PV generator, a DC-DC boos...
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| Autores principales: | , , , , , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Faculty of Technical Sciences in Cacak
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/69811408cf2e44d5b9252310f8b037b6 |
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| Sumario: | Conventionally, the parameters of a fuzzy logic controller (FLC) are obtained
by a trial and error method or by human experience. In this paper, the
problem of designing a FLC for maximum power point tracking (MPPT) of a
photovoltaic system (PV) that consists of a PV generator, a DC-DC boost
converter and a lead-acid battery is studied. The normalization gains, the
membership functions and the fuzzy rules are automatically adjusted using a
particles swarm optimization algorithm (PSO) in order to maximize the
criterion based on the integration of the PV module power under standard
temperature condition (STC) (T=25ºC and S=1000 W/m2). The robustness test of
the optimized fuzzy logic MPPT controller (FLC-MPPT) is carried out under
different scenarios. Simulation results of the system clearly show that the
proposed optimized FLCMPPT controller outperforms in terms of maximum
efficiency the FLC-MPPT controller not optimized, the FLC-MPPT controller
with optimized normalization gains and the FLC-MPPT controller with
optimized normalization gains and membership functions. |
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