Global MPPT Optimization for PV Modules in Partial Shading: Corona Virus Search Optimizer

Solar energy represents a sustainable and inexhaustible source of renewable power, offering a path to clean and cost-effective electricity generation. Nonetheless, photovoltaic (PV) systems encounter a substantial challenge in the form of a nonlinear maximum power point tracking (MPPT) problem, primarily stemming from partial shading(PS) conditions. The presence of PS results in the loss of available power, undermining the efficiency of PV systems. In response to this challenge, this paper introduces a pioneering solution in the form of a Corona Virus Search Optimizer (CVSO)-based MPPT controller. This controller is meticulously designed to facilitate efficient maximum power tracking across diverse weather conditions. Its efficacy is validated through a comprehensive comparative study against two prominent optimization techniques: Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO). Through meticulous analytical and statistical analysis, the research evaluates the performance of the CVSO-based MPPT controller in two distinct scenarios: PS and complex-PS (CPS). The findings substantiate the superior capabilities of the proposed CVSO, showcasing its adeptness in swiftly and accurately tracking the maximum power point, thereby achieving faster convergence toward the global maximum power point.