Rizki Mendung Ariefianto, Indra Setyawan, Tri Nurwati, Rini Nur Hasanah, Makbul Anwari Muhammad Ramli
Solar energy converted by PV systems is very promising as the primary electricity source in rural areas. However, the varying characteristics of nature make the optimal efficiency of PV systems hard to achieve. Maximum Power Point Tracking (MPPT), as a strategy to help PV systems extract solar power efficiently, faces an issue in reaching the Global Maximum Power Point (GMPP). Many metaheuristic-based algorithms have been developed to ensure the maximum power of PV systems can be achieved. This study aims to evaluate the performance of a metaheuristic-based MPPT called Gravitational Search Optimization (GSO), which is applied to PV systems. As a comparison, Particle Swarm Optimization (PSO), another MPPT strategy, was employed. The results show that the GSO exhibits superior tracking ability and convergence rate compared to the PSO. This means that GSO has proven its capability to reach GMPP, leading to beneficial improvements in overall PV system performance. © 2024 IEEE.
Universitas Brawijaya, Department of Electrical Engineering, Malang, Indonesia; King Abdulaziz University, Department of Electrical and Computer Engineering, Jeddah, Saudi Arabia
