PSO-AFSA Global Maximum Power Point Tracking Algorithm with Adaptive Evolutionary Strategy for PV System

Abstract

The P-U curve of the PV (photovoltaic) system has multi-peak characteristics under non-uniform irradiance conditions (NUIC). The conventional MPPT algorithm can only track the local maximum power points, therefore, PV system fails to work at the global optimum, causing serious energy loss. How to track its global maximum power point is of great significance for the PV system to maintain an efficient output state. PSO-AFSA (Particle Swarm Optimization Artificial Fish Swarm Algorithm) is a global maximum power point tracking (GMPPT) algorithm with strong global search capability, but the convergence speed and accuracy of the algorithm are limited. To solve the mentioned problems, a modified AESPSO-AFSA GMPPT algorithm is proposed in this paper by introducing the evolution strategy into PSO-AFSA algorithm. Simulation results show that under NUIC, compared with the conventional P&O and PSO-AFSA algorithm, the proposed algorithm has well performance on getting out of the local optimal solution and improving the global optimal solution of the individual neighborhood, the convergence speed and convergence accuracy are also increased.