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

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