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 is crucial to identify the parameters of PV models. Recently, various metaheuristic methods have been propo...

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Veröffentlicht in:Soft computing (Berlin, Germany) Jg. 25; H. 20; S. 12875 - 12898
Hauptverfasser: Li, Miao, Li, Chunquan, Huang, Zhengyu, Huang, Jiehui, Wang, Gaige, Liu, Peter X.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.10.2021
Schlagworte:
Artificial Intelligence
Computational Intelligence
Control
Engineering
Mathematical Logic and Foundations
Mechatronics
Optimization
Robotics
Chicken swarm optimization algorithm (CSO)
Spiral shrinkage
Photovoltaic models
Parameters identification
ISSN:1432-7643, 1433-7479
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Zusammenfassung: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 is crucial to identify the parameters of PV models. Recently, various metaheuristic methods have been proposed to identify the parameters, but they cannot provide sufficient accurate and reliable performance. To address this problem, this paper proposes a spiral-based chaos chicken swarm optimization algorithm (SCCSO) including three strategies: (1) the information-sharing strategy provides the latest information of the roosters for searching global optimal solution, beneficial to improve the exploitation ability; (2) the spiral motion strategy can enable hens and chicks to move toward their corresponding targets with a spiral trajectory, improving the exploration ability; and (3) a self-adaptive-based chaotic disturbance mechanism is introduced around the global optimal solution to generate a promising solution for the worst chick at each iteration, thereby improving the convergence speed of the chicken flock. Besides, SCCSO is used for identifying different PV models such as the single-diode, the double-diode, and PV module models. Comprehensive analysis and experimental results show that SCCSO provides better robustness and accuracy than other advanced metaheuristic methods.
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-021-06010-x