Application of a Novel Meta-heuristic Optimization Algorithm for Solar PV Module MPPT Tracking Under the Influence of Environmental Conditions

The researchers have proposed numerous meta-heuristic optimization algorithms in the scenario of rapid improvements in artificial intelligence and machine learning environments. An application of maximum power point tracking (MPPT) for a solar photovoltaic with a recently developed meta-heuristic optimization algorithm has been considered as a case study in this paper. The analysis has been presented for the effective system study and to match the practical condition by considering environmental effects such as partial shading conditions. Though the conventional approaches of MPPT serve the purpose, they are limited to idle conditions where shading conditions have not been considered. Hence, this paper solves the limitations and presents an efficient solution for extracting MPPT under partial shading conditions through Autonomous Particles Groups Particle Swarm Optimization (AGPSO) algorithm. The results presented in the paper also support the proposed meta-heuristic approach by drawing maximum power from the solar PV module. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.