An improved grey wolf optimization algorithm for the maximum power point tracker of pv system under various illumination conditions

The maximum power point tracker (MPPT) has been a crucial part in the photovoltaic (PV) control system for its stable and efficient operation. Although the maximum power point (MPP) of PV array under uniform illumination can be reached easily through the classical MPPT algorithms, when it comes to the partially sheltered and various illumination conditions, the acquisition of the global maximum power point (GMPP) will be interfered from the appearances of multiple local maximum power points. Therefore, an improved grey wolf optimization (IGWO) algorithm is proposed for the design of the MPPT of PV system under various irradiation conditions. In the population initialization of IGWO, chaotic mirroring method is used to replace the random initialization of traditional GWO for the promotion of the population diversity and superiority. Besides, the control parameter of IGWO is changed along a cosine trend, which has broadened the search breadth of the previous optimization process and improved the convergence precision of the later optimization process. In addition, a threshold is defined to judge whether the search process falls into local optimum. If the local optimum has been verified, polynomial mutation is constructed to force the current solution bounces local optimum. Then, the proposed IGWO algorithm is applied in the design of MPPT in PV system. Finally, the fast speed and high precision of the proposed MPPT in the tracking process of GMPP are proved through simulation experiments. © 2020, Chinese Institute of Electrical Engineering. All rights reserved.