Design and Evaluation of Fuzzy Adaptive Particle Swarm Optimization Based Maximum Power Point Tracking on Photovoltaic System Under Partial Shading Conditions

Artificial intelligence methods such as fuzzy logic and particle swarm optimization (PSO) have been applied to maximum power point tracking (MPPT) for solar panels. The P-V curve of a panel exhibits multiple peaks under partial shading condition (PSC) when all modules do not receive the same irradiation. Although conventional PSO has shown perform well uniform insolation, it is often unable find global PSC. Fuzzy adaptive controllers proposed MPPT. However, controller became computation-intensive in order adjust parameters each particle. In this paper, PSO-based MPPT are compared evaluated aspect design performance. A simple was designed reach optimal PSC combines advantages both control. dynamically adjusts parameter improve convergence speed search capability. Since tuning be common particles, reduced computation complexity. controller’s rule base obtain fast transient response stable steady state response. Design verified with simulation results using boost converter. comparison Simulation shows able process increase convergency speed. indicates settling time 14% faster on average 30% irradiation than PSO. Both similar output accuracy.