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.
Citation: Shijian Chen, Yongquan Zhou, Qifang Luo. Hybrid adaptive dwarf mongoose optimization with whale optimization algorithm for extracting photovoltaic parameters[J]. AIMS Energy, 2024, 12(1): 84-118. doi: 10.3934/energy.2024005
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.
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Shijian Chen, Yongquan Zhou, Qifang Luo. Hybrid adaptive dwarf mongoose optimization with whale optimization algorithm for extracting photovoltaic parameters[J]. AIMS Energy, 2024, 12(1): 84-118. doi: 10.3934/energy.2024005
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