Abstract
Photovoltaic (PV) systems are among the representatives of renewable energy technologies, and their performance is influenced by parameter configurations. This paper utilizes the swarm-elite learning mechanism’s Levy flight and Quadratic interpolation strategies to enhance the optimization performance of the Hippopotamus Optimization algorithm (HO) in both the exploration and exploitation stages. The proposed algorithm is referred to as LQHO. The aim of this paper is to utilize LQHO to provide a high-quality solution for parameter extraction problems in various types of PV models. Ten representative CEC2017 functions and three PV models are selected for designing experiments to evaluate the optimization performance and parameter extraction capability of LQHO. Five advanced metaheuristic algorithms are chosen to design control group experiments. The results indicate that LQHO exhibits superior performance over its competitors in both the parameter extraction problems of the ten CEC2017 functions and the three PV models. This superiority is reflected in terms of solution accuracy, convergence speed, and robustness.
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Wang, W., Tian, J. (2024). An Improved Hippopotamus Optimization Algorithm Utilizing Swarm-Elite Learning Mechanism’s Levy Flight and Quadratic Interpolation Operators for Optimal Parameters Extraction in Photovoltaic Models. In: Huang, DS., Zhang, X., Chen, W. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14862. Springer, Singapore. https://doi.org/10.1007/978-981-97-5578-3_21
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DOI: https://doi.org/10.1007/978-981-97-5578-3_21
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