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<p>The key to realize the accurate modeling of photovoltaic (PV) systems depends on extracting high-precise unknown parameters of equivalent circuit models for PV cells. However, high-precise values for these unknown parameters are hard to achieve because of PV cells’ highly nonlinear dynamic characteristics. Whale optimization algorithm (WOA) gets regarded as one promising approach for PV cell models’ parameter extraction because of its good search performance. Nevertheless, WOA provides strong local exploitation but exhibits low exploration, which leads to premature convergence. To address this issue, this work puts forward an enhanced WOA named RLEWOA. On the one hand, an improved ranking-based differential mutation operator is designed for enhancing the exploration capability. This operator can select random yet superior individuals to steer the movement of target individuals in the promising direction. On the other hand, reinforcement learning is integrated to help the target individuals select more suitable position update strategies at different stages for better balancing exploitation and the exploration. The resultant approach is implemented for nine cases and analyzed in comparison with other algorithms. Simulation results show that RLEWOA significantly outperforms other algorithms and demonstrates very high accuracy for extracting PV cell models’ parameters.</p>

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Reinforcement learning whale optimization algorithm-based optimal equivalent circuit models for photovoltaic cells and modules

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月21日

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  • Guojiang Xiong,
  • Sha Yang,
  • Jun Chen,
  • Ali Wagdy Mohamed

摘要

The key to realize the accurate modeling of photovoltaic (PV) systems depends on extracting high-precise unknown parameters of equivalent circuit models for PV cells. However, high-precise values for these unknown parameters are hard to achieve because of PV cells’ highly nonlinear dynamic characteristics. Whale optimization algorithm (WOA) gets regarded as one promising approach for PV cell models’ parameter extraction because of its good search performance. Nevertheless, WOA provides strong local exploitation but exhibits low exploration, which leads to premature convergence. To address this issue, this work puts forward an enhanced WOA named RLEWOA. On the one hand, an improved ranking-based differential mutation operator is designed for enhancing the exploration capability. This operator can select random yet superior individuals to steer the movement of target individuals in the promising direction. On the other hand, reinforcement learning is integrated to help the target individuals select more suitable position update strategies at different stages for better balancing exploitation and the exploration. The resultant approach is implemented for nine cases and analyzed in comparison with other algorithms. Simulation results show that RLEWOA significantly outperforms other algorithms and demonstrates very high accuracy for extracting PV cell models’ parameters.