关键词: 最大功率点追踪, 灰狼算法, 粒子群算法, 局部阴影

Abstract:  Under local shading conditions, the power-voltage characteristic curves of photovoltaic arrays show multiple peaks, and traditional population intelligence optimization suffers from slow convergence, large oscillation amplitude and the tendency to fall into local optimality. To address the above problems, an MPPT (Maximum Power Point Tracking)control method based on the PSO-GWO(Particle Swarm Optimization-Grey Wolf Optimization) algorithm is proposed. The algorithm introduces a convergence factor that varies with the cosine law to balance the global search and local search ability of the GWO algorithm; the PSO algorithm is introduced to improve the information exchange between individual grey wolves and their own experience. Simulation results show that the proposed PSO-GWO algorithm not only converges quickly under local shading conditions, but also has a smaller power output oscillation amplitude, effectively improving the maximum power tracking efficiency and accuracy of the PV(Photovoltaic) array under local shading conditions. 

Key words: maximum power tracking, grey wolf algorithm, particle swarm algorithm, partial shadow