An Enhanced Drift-Free Perturb and Observe Maximum Power Point Tracking Method Using Hybrid Metaheuristic Algorithm for a Solar Photovoltaic Power System

Despite of being a cleaner energy resource, the solar photovoltaic (SPV) system faces the dynamic and unpredictable changes in environmental conditions; hence, conventional and extant maximum power point tracking (MPPT) methods can get stuck at local minima. However, on account of less switching strain on the DC-DC converter, the drift-free P&O MPPT method can be supervised with effective bio-inspired metaheuristic algorithms to maximize its robustness and efficiency to generate photovoltaic power. Therefore, in this paper, the efficiency of the drift-free P&O MPPT method is significantly enhanced using a grey wolf skill embedded levy flight optimization (LI-GWO) method as a new approach. Firstly, a single-ended primary inductor converter (SEPIC)-based grid-connected SPV system is modeled to assess the MPPT performance. Further, using the LI-GWO enhanced drift-free P&O algorithm, the duty cycle of the SEPIC is regulated by updating the position of the grey wolfs based on the Brownian motion of the levy flights. Moreover, the exploration, exploitation and convergence analysis are carried out to examine the effectiveness of the proposed LI-GWO + drift-free P&O algorithm. In this manner, the proposed algorithm attains the global maxima quickly and, thereafter, the global MPP (GMPP) is tracked by the drift-free P&O itself with the less switching strain. The performance of the proposed MPPT approach is compared with the other conventional and hybrid metaheuristic-based MPPTs to show effectiveness under the newly formulated extreme weather condition model.