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<p>To effectively enhance the conversion of solar energy into electrical power, it is imperative to accurately determine the appropriate equivalent circuit parameters for modeling, estimating, and tracking the maximum power point of photovoltaic (PV) systems with high precision and efficiency. This article proposes an advanced hybrid optimization method, termed Enhanced Hybrid Tuna Swarm Optimization based on the Equilibrium Optimizer (EOTSO), aimed at precisely identifying the parameters of PV systems. By integrating an Equilibrium Optimizer (EO) component, the EOTSO algorithm is better equipped to explore promising solution ranges within the local region, thereby improving the accuracy of local optimal solutions. The effectiveness of the developed EOTSO technique is demonstrated through five case studies: the Single Diode Model (SDM) and the Double Diode Model (DDM) for RTC France solar cell, SDM and DDM for the PWP-201 polycrystalline solar panel and SDM for SM55 module with various temperature and irradiance levels. On the&#xa0;one hand, the efficacy of EOTSO is evaluated in solving high-dimensional numerical optimization problems, including CEC benchmark functions with 30, 50, and 100 dimensions, as well as identifying parameters in these cases using various established and recently introduced metaheuristic algorithms. Experimental results across different PV models validate EOTSO's superiority in terms of accuracy, robustness, stability, and convergence speed. The RMSEs of the EOTSO on the SDM and the DDM of RTC France solar cell and PWP-201 polycrystalline solar panel were 9.8602E−04, 9.77696E−04, 2.42507E−03, and 2.42507E−03, respectively. Furthermore, the developed technique emerges as a superior alternative for parameter identification in photovoltaic modules. Statistical analyses indicate that EOTSO significantly outperforms conventional Tuna Swarm Optimization (TSO) and other techniques, establishing itself as a prominent and capable tool for addressing a wide range of optimization challenges.</p>

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A hybrid approach based on tuna swarm and equilibrium optimizers for parameter estimation of different PV models

  • 期刊论文

  • 发表日期：

    2025年2月25日

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  • Mohamed H. Hassan,
  • Salah Kamel

摘要

To effectively enhance the conversion of solar energy into electrical power, it is imperative to accurately determine the appropriate equivalent circuit parameters for modeling, estimating, and tracking the maximum power point of photovoltaic (PV) systems with high precision and efficiency. This article proposes an advanced hybrid optimization method, termed Enhanced Hybrid Tuna Swarm Optimization based on the Equilibrium Optimizer (EOTSO), aimed at precisely identifying the parameters of PV systems. By integrating an Equilibrium Optimizer (EO) component, the EOTSO algorithm is better equipped to explore promising solution ranges within the local region, thereby improving the accuracy of local optimal solutions. The effectiveness of the developed EOTSO technique is demonstrated through five case studies: the Single Diode Model (SDM) and the Double Diode Model (DDM) for RTC France solar cell, SDM and DDM for the PWP-201 polycrystalline solar panel and SDM for SM55 module with various temperature and irradiance levels. On the one hand, the efficacy of EOTSO is evaluated in solving high-dimensional numerical optimization problems, including CEC benchmark functions with 30, 50, and 100 dimensions, as well as identifying parameters in these cases using various established and recently introduced metaheuristic algorithms. Experimental results across different PV models validate EOTSO's superiority in terms of accuracy, robustness, stability, and convergence speed. The RMSEs of the EOTSO on the SDM and the DDM of RTC France solar cell and PWP-201 polycrystalline solar panel were 9.8602E−04, 9.77696E−04, 2.42507E−03, and 2.42507E−03, respectively. Furthermore, the developed technique emerges as a superior alternative for parameter identification in photovoltaic modules. Statistical analyses indicate that EOTSO significantly outperforms conventional Tuna Swarm Optimization (TSO) and other techniques, establishing itself as a prominent and capable tool for addressing a wide range of optimization challenges.