A hybrid global maximum power point tracking control method based on particle swarm optimization (PSO) and perturbation and observation (P&O)

The P-V curve of photovoltaic systems has multi-peaks characteristics under partial shading conditions, which will reduce the effectiveness of the traditional maximum power point tracking (MPPT) algorithms to find the global maximum power point (GMPP). The particle swarm optimization (PSO) algorithm has good global search ability, but may suffer from low convergence speed. The combination of PSO and perturbation and observation (P&O) algorithms can behave strong GMPP search ability, fast convergence and simple calculation, and thus can improve the accuracy of MPPT. In this paper, an improved PSO-P&O hybrid algorithm is proposed. A new initialization particle method is employed to reduce the number of particles and a novel PSO to P&O transition method is adopted to speed up convergence. Moreover, reinitialization is used to avoid the system dropping to the local maximum power point (LMPP) when the light intensity is changed. The feasibility and effectiveness of the proposed algorithm is verified by the MATLAB simulation. The simulated results show that the GMPP can be found in 5–6 iterations by the proposed algorithm. The MPPT efficiency reaches 99.82 % and 99.98 % respectively under two different light intensity conditions. Consequently, the proposed algorithm can find the maximum power point (MPP) more accurately and quickly, and therefore can improve the efficiency of photovoltaic system under partial shading conditions.