Global Maximum Power Point Tracking Algorithm for Solar Power 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. Artificial Fish Swarm Algorithm (AFSA) 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 Hybrid Artificial Fish Swarm Algorithm (HAFSA) for GMPPT is proposed in this paper by using formulation of the Particle Swarm Optimization (PSO) to reformulate the AFSA and improving the principal parameters of the algorithm. Simulation results show that when under NUIC, compared with the PSO and AFSA algorithm, the proposed algorithm has well performance on the convergence speed and convergence accuracy.