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 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=25?C 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.