Maximum power point tracking under partial shading condition-based on Lévy flight-grey wolf optimization

Due to the multi-peak characteristics of the power curve under Partial Shading Condition (PSC) in PV power generation systems, the traditional Maximum Power Point Tracking (MPPT) technique suffers from the problem that the convergence speed and the ability to jump out of the local optimum solution are difficult to achieve. In this paper, a Lévy Flight-Grey Wolf Optimization algorithm is proposed and applied to the PV MPPT problem. The Lévy Flight strategy is used to improve the ability of the algorithm to jump out of the local optimum and enhance the global searching performance. At the same time, the social hierarchy of wolves in the Grey Wolf Optimization algorithm is improved so that the algorithm can quickly converge to the Maximum Power Point (MPP). Finally, simulations are carried out under static and varying partial shading conditions and compared with Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). The proposed algorithm is verified to be more effective in terms of convergence speed, global search capability and robustness