A comprehensive comparison of advanced metaheuristic photovoltaic maximum power tracking algorithms during dynamic and static environmental conditions.

This study introduces a novel technique for achieving the global peak (GP) in solar photovoltaic (PV) systems under partial shadowing conditions (PSC) using the Dandelion Optimizer Algorithm (DOA), inspired by the dispersal of dandelion seeds in the wind. The proposed approach aims to enhance the power generation efficiency of PV systems across various scenarios, including dynamic uniform, dynamic PSCs, static uniform irradiances, and static PSCs. The proposed approach improves tracking efficiency, provides non-oscillatory steady-state responses, and reduces transients as well as enhancing the dynamic performance of the whole system. Simulation and hardware-in-loop (HIL) experiments demonstrate that the DOA outperforms several state-of-the-art techniques, such as hybrid grey wolf optimizer since-cosine algorithm (HGWOSCA), grasshopper optimization algorithm (GOA), dragonfly optimizer (DFO), particle swarm optimizer with gravitational search (PSOGS), PSO, cuckoo search algorithm (CSA), perturb &observe (P&O), and incremental conductance (INC), achieving average efficiencies of 99.93 %, 88.84 %, 94.48 %, 87.12 %, 88.05 %, 94.79 %, 93.82 %, 85.25 %, and 77.93 %, respectively. These results underscore the DOA's effectiveness in improving maximum power point tracking (MPPT) performance in solar arrays, particularly under challenging dynamic PSC conditions.