Photovoltaic MPPT adaptive particle swarm optimization optimization under shading conditions

In view of the multi-peak characteristic of the power-voltage characteristic curve of photovoltaic array in the case of local shadow, particle swarm optimization algorithm is applied to MPPT tracking in the case of local shadow, which has the disadvantages of slow search speed and low accuracy. The maximum power point tracking algorithm of adaptive inertia weight particle swarm optimization algorithm is proposed to automatically update the inertia weights w and learning factors C1 and C2. Through simulation experiments, the GMPP time before optimization is 0.045 s and the output power is 468 W. After optimization, the GMPP time of the adaptive particle swarm algorithm is 0.02 s and the output power is stabilized at 480 W. The output power tracking error of the PV array is less than 30%. In the simulation by the irradiance mutation model established in this paper, the tracking time is reduced by 55.5% and the tracking error is less than 5% compared with the traditional particle swarm algorithm. The feasibility and practicability of the algorithm are verified. © 2022, Solar Energy Periodical Office Co., Ltd. All right reserved.