An improved MPPT method for photovoltaic systems based on mayfly optimization algorithm
Shixun Mo, Qintao Ye, Kunping Jiang, Xiaofeng Mo, Gengyu Shen
Abstract
Because the traditional maximum power point tracking (MPPT) of photovoltaic (PV) array is easy to fail in complex environment, and the existing intelligent optimization algorithms have shortcomings such as slow convergence speed, low convergence precision and poor stability. Therefore, this paper proposes a new intelligent optimization algorithm MPPT tracking method. Based on mayfly intelligent optimization algorithm, this method can track the maximum power point (MPP) of PV system by two populations, which can quickly and accurately track MPP of PV system under complex environmental conditions. By using Matlab/Simulink simulation platform, the method was studied under uniform illumination and non-uniform illumination respectively, and its effectiveness was verified. Compared with the particle swarm optimization algorithm (PSO), its superiority is verified.