Determining Optimal Membership Functions of a FLC-based MPPT Algorithm Using the Particle Swarm Optimization Method

Fuzzy-logic-control (FLC)-based maximum power point tracking (MPPT) algorithm can successfully deal with the transient time/tracking accuracy dilemma of the commonly utilized perturb and observe (PaO) method, however, optimal setting of the membership functions (MFs) is hard to find. In this paper, particle swarm optimization (PSO) technique is adopted to determine the optimal input MF setting values. According to the simulated and experimental results, the obtained optimal input MF values can improve the averaged MPPT tracking accuracy by 1.31 %. Moreover, the averaged fitness value can significantly be improved by 25.6 %.