Comparative study of nature-inspired maximum power point tracking algorithms for partially shaded photovoltaic systems

Abstract

Photovoltaic (PV) systems are widely used for converting solar energy into electrical energy. However, PV systems are susceptible to partial shading, leading to fluctuations in temperature and irradiation that degrade the system's performance. To overcome this challenge, maximum power point tracking (MPPT) algorithms are implemented in PV systems. This research paper provides a comprehensive comparative analysis of three nature-inspired MPPT algorithms, namely cuckoo search, grey wolf and fish swarm optimization, to improve the performance of PV systems under partially shaded conditions. The study evaluates the speed, complexity, compatibility, and stability of each algorithm, and concludes that the fish swarm optimization algorithm is the most effective among the three. The novelty of this research lies in the in-depth comparison of nature-inspired MPPT algorithms (specifically fish swarm optimization) for partially shaded PV systems, offering valuable insights for researchers to improve the performance of PV systems.