关键词: ：最大功率点跟踪, 粒子群算法, 自适应步长扰动观察法, 惯性权重, 局部阴影

Abstract: Under uneven illumination conditions, the output characteristics of photovoltaic (PV) arrays exhibit multiple peaks, and the traditional maximum power point tracking (MPPT) methods are prone to local extreme values. In order to improve tracking speed and accuracy under complex shadows, this paper proposes an improved MPPT control method based on the improved particle swarm optimization and adaptive perturbation and observation (IPSO-AP&O) algorithm by combining the advantages of IPSO and AP&O. Specifically, IPSO adopts an adaptive inertia weight factor, which can fully search the objective function in different search stages, and then combines with AP&O to achieve stable output of the maximum power. The simulation results show that the proposed IPSO-AP&O algorithm reduces the iteration process of the traditional intelligent algorithms, and can quickly track the global maximum power point. Compared with other algorithms, it has fast and accurate dual tracking abilities when the illumination intensity changes abruptly. The tracking efficiencies in four scenarios are 99.86%, 99.91%, 87.63% and 99.79% respectively, which can better reduce the power loss caused by the change of external conditions of PV arrays. Moreover, the proposed MPPT control method can be applied to PV storage hybrid systems and has certain engineering practical value.