A New Hybrid Algorithm for Power Optimization in Shaded Photovoltaic Systems

This paper describes a unique Maximum Power Point Tracking (MPPT) approach that combines two sophisticated optimization techniques, Grey Wolf Optimizer (GWO) and Particle Swarm Optimization (PSO). The suggested hybrid technique, GWO-PSO, uses the benefits of both algorithms to increase exploration and exploitation capabilities, resulting in faster convergence and more accuracy in discovering optimum solutions. The GWO-PSO algorithm's performance is assessed under Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its efficacy in global and local searches. Experiments were carried out over three PSC situations, comparing the hybrid algorithm against traditional MPPT approaches such as PSO and GWO. The results showed that GWO-PSO beat these traditional approaches, with an average tracking time of 0.267 seconds and an impressive efficiency rate of 99.97 %. These findings demonstrate the algorithm's capacity to swiftly and correctly identify the Global Maximum PowerPoint (GMPP). This makes it a highly efficient and dependable method for increasing MPPT performance in solar systems. This study focuses on GWO-PSO's capacity to improve energy efficiency and sustainability in renewable energy applications, particularly solar energy systems, under dynamic environmental circumstances. The algorithm's enhanced tracking capability aids in more effective solar energy collection, improving the field of renewable energy optimization.