Enhancing Global MPPT Efficiency for PV Panels with Partial Shading Using Crow Search Algorithm

This paper presents an innovative approach to optimizing the performance of photovoltaic (PV) systems under partial shading (PS) conditions using a Crow Search Algorithm (CSA)-based Maximum Power Point Tracking (MPPT) controller. Solar energy is a key renewable resource, but PV systems often face efficiency challenges due to the difficulty of tracking the maximum power point, especially in nonlinear and shaded environments. The proposed CSA-based controller is specifically developed to handle these challenges by accurately and efficiently tracking the global maximum power point (GMPP) under varying weather and shading conditions. A comprehensive comparison with two other optimization methods, Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CuSA), is conducted to assess the controller's performance. The findings demonstrate that the CSA-based MPPT controller achieves faster convergence and greater accuracy in both partial shading and complex shading scenarios. With fewer oscillations and quicker response times, the CSA approach proves to be highly effective in improving the overall energy efficiency of PV systems. This study positions the CSA-based MPPT controller as a promising solution for advancing solar energy utilization in complex environmental conditions.