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Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models

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  • Yingjie Song
  • Daqing Wu
  • Ali Wagdy Mohamed
  • Xiangbing Zhou
  • Bin Zhang
  • Wu Deng
  • Ahmed Mostafa Khalil

Abstract

In the past few decades, a lot of optimization methods have been applied in estimating the parameter of photovoltaic (PV) models and obtained better results, but these methods still have some deficiencies, such as higher time complexity and poor stability. To tackle these problems, an enhanced success history adaptive DE with greedy mutation strategy (EBLSHADE) is employed to optimize parameters of PV models to propose a parameter optimization method in this paper. In the EBLSHADE, the linear population size reduction strategy is used to gradually reduce population to improve the search capabilities and balance the exploitation and exploration capabilities. The less and more greedy mutation strategy is used to enhance the exploitation capability and the exploration capability. Finally, a parameter optimization method based on EBLSHADE is proposed to optimize parameters of PV models. The different PV models are selected to prove the effectiveness of the proposed method. Comparison results demonstrate that the EBLSHADE is an effective and efficient method and the parameter optimization method is beneficial to design, control, and optimize the PV systems.

Suggested Citation

  • Yingjie Song & Daqing Wu & Ali Wagdy Mohamed & Xiangbing Zhou & Bin Zhang & Wu Deng & Ahmed Mostafa Khalil, 2021. "Enhanced Success History Adaptive DE for Parameter Optimization of Photovoltaic Models," Complexity, Hindawi, vol. 2021, pages 1-22, January.
  • Handle: RePEc:hin:complx:6660115
    DOI: 10.1155/2021/6660115
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    Cited by:

    1. Meshari Alsharari & Ammar Armghan & Khaled Aliqab, 2023. "Numerical Analysis and Parametric Optimization of T-Shaped Symmetrical Metasurface with Broad Bandwidth for Solar Absorber Application Based on Graphene Material," Mathematics, MDPI, vol. 11(4), pages 1-15, February.
    2. Fan, Yi & Wang, Pengjun & Heidari, Ali Asghar & Chen, Huiling & HamzaTurabieh, & Mafarja, Majdi, 2022. "Random reselection particle swarm optimization for optimal design of solar photovoltaic modules," Energy, Elsevier, vol. 239(PA).
    3. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    4. Arooj Tariq Kiani & Muhammad Faisal Nadeem & Ali Ahmed & Irfan A. Khan & Hend I. Alkhammash & Intisar Ali Sajjad & Babar Hussain, 2021. "An Improved Particle Swarm Optimization with Chaotic Inertia Weight and Acceleration Coefficients for Optimal Extraction of PV Models Parameters," Energies, MDPI, vol. 14(11), pages 1-24, May.
    5. Zaiyu Gu & Guojiang Xiong & Xiaofan Fu, 2023. "Parameter Extraction of Solar Photovoltaic Cell and Module Models with Metaheuristic Algorithms: A Review," Sustainability, MDPI, vol. 15(4), pages 1-45, February.

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