AUTHOR=Yan Chang , Lei Guoping , Cai Li , He Chao , Dai Nina , Jiang Zhou , Wu Jiacheng , Li Shenghao TITLE=MPPT control technology based on the GWO-VINC algorithm JOURNAL=Frontiers in Energy Research VOLUME=11 YEAR=2023 URL=https://www.frontiersin.org/journals/energy-research/articles/10.3389/fenrg.2023.1205851 DOI=10.3389/fenrg.2023.1205851 ISSN=2296-598X ABSTRACT=

It is a challenging task to accurately track the global maximum power point (GMPP) in a changing environment in widely used photovoltaic (PV) systems. So far, a variety of maximum power point (MPP) tracking algorithms have been used in solar PV power systems. The classical algorithm is simple and fast to track the speed effectively in a constant environment, but it can get stuck at an extreme point in a variable environment. In this paper, the variable-step incremental conductance (VINC) method is combined with the gray wolf optimization (GWO) algorithm. Firstly, GWO conducts a global search. When the search reaches the area near GMPP, the next step of the search process is carried out based on the iteration number conditions of GWO. Enter the search process of VINC and determine whether the current search process is on the left or right side of the vertex based on the current search value. And adjust the duty cycle during the VINC search process using different variable step size methods based on the left and right sides, and finally accurately locate the GMPP value. To verify the robustness of the proposed algorithm, simulation, and experimental comparisons were conducted between the proposed method in the article and GWO and VINC. The tracking efficiency of static shadows, simulated dynamic shadows, and experimental static and dynamic shadows is 99.80%, 98.82%, 99.43%, and 98.51%, respectively. The tracking time of simulation and experiment is 46.49% and 89.34% faster than GWO and VINC technologies, respectively. The results show that compared with the GWO and VINC methods, the proposed method has improved tracking speed and efficiency. Moreover, compared with the method that combines the two intelligent algorithms, this method has fewer algorithm parameters, a simple calculation process, lower complexity, lower hardware requirements, and better actual implementation performance.