Power Harvested Maximization for Solar Photovoltaic Energy System Under Static and Dynamic Conditions

Photovoltaic (PV) systems are increasingly recognized as a viable renewable energy source due to their clean, abundant, silent, and environmentally friendly nature. However, their efficiency is significantly influenced by environmental conditions, necessitating advanced control strategies to ensure optimal power extraction. This study aims to enhance the performance of PV systems by developing and evaluating maximum power point tracking (MPPT) algorithms capable of operating effectively under both uniform irradiance and partial shading conditions (PSCs). Specifically, two metaheuristic algorithms—Particle Swarm Optimization (PSO) and Cuckoo Search Optimization (CSO)—are modeled, implemented, and tested for tracking the global peak power (GPP) in various static and dynamic scenarios. Simulation results indicate that both algorithms accurately and efficiently track the GPP under static uniform and PSCs. Under dynamic conditions, while both the PSO and CSO can initially locate the GPP, they fail to maintain accurate tracking during subsequent intervals. Notably, CSO exhibits reduced oscillations and faster response time compared with PSO. These findings suggest that while metaheuristic MPPT methods are effective in static environments, their performance in dynamic conditions remains a challenge requiring further enhancement.