Photovoltaic GMPPT Control Method under Local Shade Based on Improved DBO

In order to further improve the tracking accuracy, speed, and disturbance robustness of the global maximum power point tracking (GMPPT) control of a photovoltaic array under partial-shade conditions, a photovoltaic GMPPT control method based on the improved dung beetle optimization (IDBO) algorithm is proposed. First, in order to improve the algorithm performance, a Chebyshev chaotic map is used to initialize the positions of the dung beetles to make the distribution of the dung beetle population in the search space more uniform, which increased the population convergence rate and final solution accuracy of the algorithm. Second, a neighborhood search mechanism combined with a Levy flight strategy is introduced to enhance the local search precision and convergence speed of the algorithm, and improve the accuracy of the global maximum power point (GMPP) location. At the same time, a dynamic weight is introduced to improve the convergence rate of the algorithm in the later search stage, along with the global-search ability of the equalization algorithm. Finally, a restart mechanism is used to enhance the robustness of the GMPPT control through the influence of mutation factors in a complex environment. The experimental results show that compared with DBO, grey wolf optimization (GWO), sparrow search algorithm (SSA) and particle swarm optimization (PSO) algorithms, the IDBO based solar maximum power point tracking control method is more accurate in determining the GMPP position, and has better dynamic response speed and tracking accuracy.