Hybrid adaptive dwarf mongoose optimization with whale optimization algorithm for extracting photovoltaic parameters

This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to enhance the capability of DMO in escaping local optima, while introducing inertial w...

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Published in:AIMS energy Vol. 12; no. 1; pp. 84 - 118
Main Authors: Chen, Shijian, Zhou, Yongquan, Luo, Qifang
Format: Journal Article
Language:English
Published: AIMS Press 2024
Subjects:
dwarf mongoose optimization
hybrid metaheuristic algorithm
parameters extraction
photovoltaic models
whale optimization algorithm
ISSN:2333-8334, 2333-8334
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Abstract This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to enhance the capability of DMO in escaping local optima, while introducing inertial weights to achieve a balance between exploration and exploitation. The DMOWOA performances are tested through the solving of the single diode model, double diode model, and photovoltaic (PV) modules. Finally, the DMOWOA is compared with six well-known algorithms and other optimization methods. The experimental results demonstrate that the proposed DMOWOA exhibits remarkable competitiveness in convergence speed, robustness, and accuracy.
AbstractList This article proposed adaptive hybrid dwarf mongoose optimization (DMO) with whale optimization algorithm (DMOWOA) to extract solar cell model parameters. In DMOWOA, the whale optimization algorithm (WOA) is used to enhance the capability of DMO in escaping local optima, while introducing inertial weights to achieve a balance between exploration and exploitation. The DMOWOA performances are tested through the solving of the single diode model, double diode model, and photovoltaic (PV) modules. Finally, the DMOWOA is compared with six well-known algorithms and other optimization methods. The experimental results demonstrate that the proposed DMOWOA exhibits remarkable competitiveness in convergence speed, robustness, and accuracy.
Author Chen, Shijian
Zhou, Yongquan
Luo, Qifang
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SubjectTerms dwarf mongoose optimization
hybrid metaheuristic algorithm
parameters extraction
photovoltaic models
whale optimization algorithm
Title Hybrid adaptive dwarf mongoose optimization with whale optimization algorithm for extracting photovoltaic parameters
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