Addressing Shading Challenges: An Improved Carpet Weaver Optimization MPPT Method for Effective Tracking in Shaded Circumstances

Abstract

Partial shading in solar energy systems presents a significant challenge for traditional maximum power point tracking (MPPT) methods, which often fail to identify the global maximum power point (GMPP) due to the presence of many local maxima in the power-voltage (P-V) curve. This paper introduces a novel modified metaheuristic-inspired Maximum Power Point Tracking (MPPT) method, called the Carpet Weaver Optimization Approach (CWOA), specifically developed to enhance tracking efficiency in shaded settings. The proposed method employs a specific metaheuristic strategy to dynamically analyze the P-V curve and precisely distinguish between local and global maxima. The method utilizes dynamic discovery processes to ensure rapid convergence to the global maximum, regardless of differing shading patterns. Comparative simulation results indicate that the improved CWOA-based method significantly outperforms with an average efficiency of 98.09% and a mean tracking time of 0.86 seconds compared to traditional and other metaheuristic MPPT techniques, such as Particle Swarm Optimization (PSO) and the Arithmetic Optimization Algorithm (AOA), showcasing enhanced tracking accuracy, swift response times, and increased power output. This work introduces an innovative method to augment the performance of photovoltaic systems in complex shadowing conditions, thereby raising the dependability and efficiency of solar energy extraction.