LAUSR

Abstract P-V characteristic of the solar cells are nonlinear and depends on the environmental conditions such as irradiations, sunlight incident angle, cell temperature, and load conditions. Therefore it is crucial to operate the photovoltaic module at its maximum power point (MPP) all the time. Hence, a Maximum power point tracking (MPPT) methods are used to maximize the PV module output power by tracking continuously the MPP. In this paper, fuzzy logic controllers (FLC) are designed for maximum power point tracking (MPPT) in a photovoltaic system and then fuzzy membership functions of the fuzzy controller are optimized using Firefly Algorithm (FA) to generate the proper duty cycle. FA which is inspired by natural species to optimize nonlinear functions is one of the most successful and low-cost algorithms in this field. Finally, PV system with FLC-FA is compared with other methods like perturbation and observation (P&O) and fuzzy controller- Particle swarm optimization (PSO). According to the simulation results, asymmetric fuzzy membership functions based on FA increase tracking speed of MPPT and improve tracking accuracy compared to P&O, symmetric fuzzy membership functions and asymmetric fuzzy membership functions based on PSO.