Spiral-based chaotic chicken swarm optimization algorithm for parameters identification of photovoltaic models

Photovoltaic (PV) systems are becoming increasingly significant because they can convert solar energy into electricity. The conversion efficiency is related to the PV models’ parameters, so it crucial identify parameters of models. Recently, various metaheuristic methods have been proposed but cannot provide sufficient accurate and reliable performance. To address this problem, paper proposes a spiral-based chaos chicken swarm optimization algorithm (SCCSO) including three strategies: (1) information-sharing strategy provides latest information roosters for searching global optimal solution, beneficial improve exploitation ability; (2) spiral motion enable hens chicks move toward their corresponding targets with trajectory, improving exploration (3) self-adaptive-based chaotic disturbance mechanism introduced around solution generate promising worst chick at each iteration, thereby convergence speed flock. Besides, SCCSO used identifying different models such as single-diode, double-diode, module Comprehensive analysis experimental results show that better robustness accuracy than other advanced methods.