An improved SA-PSO global maximum power point tracking method of photovoltaic system under partial shading conditions

Abstract

In recent years, photovoltaic power generation has been more and more popular, because it has many advantages, clean energy and widely distributed. However, in the development process photovoltaic power generation also encountered a lot of obstacles, such as tracking the global maximum power point (GMPPT). Due to the photovoltaic system will be blocked by the photovoltaic panels appear multiple peaks, in this case, the photovoltaic panels cannot work on the maximum power point (MPP) voltage, there may be limited to the local maximum power point(LMPP). The traditional algorithms cannot solve the multi-peak GMPPT problem, so the emergence of artificial intelligence algorithm. Many artificial intelligence algorithms have been invented according to the animal's life phenomenon, such as particle swarm optimization, ant colony algorithm, simulated annealing method, fuzzy control algorithm, and so on. These algorithms can solve the multi-peak GMPPT problem, but the single algorithm still has the problems with insufficient tracking precision and slow tracking speed. This paper proposes a hybrid simulated annealing algorithm and particle swarm optimization(SA-PSO) algorithm based on MPPT algorithm which is used for photovoltaic systems under mu conditions. The proposed algorithm can reduce the tracking time and increase tracking accuracy, continuously tracking GMPP. Compared with existing SA MPPT algorithm and PSO MPPT algorithm, the proposed novel technique performs better under shading conditions.