Comparative Evaluation and Optimal Selection Strategy for Enhanced PV String Performance under Irregular Shading Conditions: Application to PV Water Pumping System

Photovoltaic (PV) systems are increasingly relied upon to power wide daily applications, especially since they are considered a clean and sustainable source of electricity generation. Moreover, they are considered a savior for remote areas that lack electricity grids. Despite their advantages, this system faces several challenges during operation, so it requires using various optimization algorithms to achieve optimal utilization and obtain the highest possible PV output power. One of the most serious challenges facing the PV system is operating under Irregular Shading Conditions (ISC). It significantly decreases the PV output power level. It also causes a complication in the shape of the PV power-voltage (P–V) curve, forming one Global Maximum Power (GMP) level among several Local ones. This study presents a comprehensive comparative evaluation of three modified control strategies to achieve the optimal controller selection for optimal PV system operation. These modified strategies are the Modified Cuckoo Search Algorithm (MCSA), the Modified Grey Wolf Optimizer (MGWO), and the Modified Particle Swarm Optimization (MPSO). They were studied and analyzed under irregular shading scenarios with gradually increasing complexity of the P–V curve for the PV system. The results reveal that all three modified algorithms outperform their conventional counterparts. A critical analysis was also provided to guide the most effective strategy for controlling PV systems in various applications, contributing to better decision-making during the design process to obtain maximum efficiency. By comparing the performance of the modified strategies, we concluded that MGWO is optimal at controlling PV system performance.