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Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm

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  • Long, Wen
  • Jiao, Jianjun
  • Liang, Ximing
  • Xu, Ming
  • Tang, Mingzhu
  • Cai, Shaohong

Abstract

Estimating parameters and establishing high-accuracy and high-reliability models of photovoltaic (PV) modules by using the actual current-voltage data is important to simulate, model, and optimize the PV systems. Several meta-heuristic optimization techniques have been developed to estimate the parameters of the solar PV models. However, it is still a challenging task to accurately, reliably, and quickly estimate the unknown parameters of PV models. This paper proposes a novel hybrid seagull optimization algorithm (HSOA) for estimating the unknown parameters of PV models effectively and accurately. In proposed HSOA, the personal historical best information is embedded into position search equation to improve the solution precision. A novel nonlinear escaping energy factor based on cosine function is presented for balancing global exploration and local exploitation. The differential mutation strategy is introduced to escape from the local optima. We firstly select twelve classical benchmark test functions to investigate the feasibility of HSOA, and experimental results show that HSOA is superior to most compared methods. Then, HSOA is used for solving parameters estimation problem of three benchmark solar PV models. The comparison results demonstrate that HSOA is superior to BOA, GWO, WOA, HHO, SOA, EEGWO, and ISCA on solution quality, convergence and reliability.

Suggested Citation

  • Long, Wen & Jiao, Jianjun & Liang, Ximing & Xu, Ming & Tang, Mingzhu & Cai, Shaohong, 2022. "Parameters estimation of photovoltaic models using a novel hybrid seagull optimization algorithm," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222006636
    DOI: 10.1016/j.energy.2022.123760
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    5. Mohammed A. A. Al-qaness & Ahmed A. Ewees & Mohamed Abd Elaziz & Ahmed H. Samak, 2022. "Wind Power Forecasting Using Optimized Dendritic Neural Model Based on Seagull Optimization Algorithm and Aquila Optimizer," Energies, MDPI, vol. 15(24), pages 1-14, December.
    6. Hossam Hassan Ali & Mohamed Ebeed & Ahmed Fathy & Francisco Jurado & Thanikanti Sudhakar Babu & Alaa A. Mahmoud, 2023. "A New Hybrid Multi-Population GTO-BWO Approach for Parameter Estimation of Photovoltaic Cells and Modules," Sustainability, MDPI, vol. 15(14), pages 1-33, July.
    7. Mina Nezamisavojbolaghi & Erfan Davodian & Amal Bouich & Mouhaydine Tlemçani & Oumaima Mesbahi & Fernando M. Janeiro, 2023. "The Impact of Dust Deposition on PV Panels’ Efficiency and Mitigation Solutions: Review Article," Energies, MDPI, vol. 16(24), pages 1-19, December.
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