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An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models

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  • Muangkote, Nipotepat
  • Sunat, Khamron
  • Chiewchanwattana, Sirapat
  • Kaiwinit, Sirilak

Abstract

Solar cells are one of the renewable energy sources that have been widely used. The parameters extraction plays an important role in the speed and accuracy of models designed for photovoltaic (PV) solar cells and modules. In recent years, the evolutionary algorithm (EA), swarm intelligence (SI), and other nature-inspired (NI) algorithms have been widely used for the parameters extraction of PV modules. This paper presents a new method by improving the existing Rcr-IJADE with an onlooker-ranking-based mutation scheme. This mutation scheme is an effective and efficient vectors selection mechanism for encountering the objective function containing a flat basin. The improved algorithm referred to as ORcr-IJADE, it is quickly and accurately extracted the parameters of solar cell models. 18 solar cell models and PV modules from several manufacturers were used to validate the algorithm. Comparative studies among the different algorithms were conducted using current-voltage (I-V) data. The results of ORcr-IJADE were compared with 31 state-of-the-art EA, SI, and NI algorithms. The results confirm the superiority of the proposed method, as the accuracy, the success rate and the convergence speed are better than the competitors. The proposed algorithm is useful in developing highly accurate solar PV models with less computational effort used.

Suggested Citation

  • Muangkote, Nipotepat & Sunat, Khamron & Chiewchanwattana, Sirapat & Kaiwinit, Sirilak, 2019. "An advanced onlooker-ranking-based adaptive differential evolution to extract the parameters of solar cell models," Renewable Energy, Elsevier, vol. 134(C), pages 1129-1147.
    • Handle: RePEc:eee:renene:v:134:y:2019:i:c:p:1129-1147
    DOI: 10.1016/j.renene.2018.09.017
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    References listed on IDEAS

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    Cited by:

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    3. Mehmet Yesilbudak, 2021. "Parameter Extraction of Photovoltaic Cells and Modules Using Grey Wolf Optimizer with Dimension Learning-Based Hunting Search Strategy," Energies, MDPI, vol. 14(18), pages 1-27, September.
    4. Ridha, Hussein Mohammed & Hizam, Hashim & Gomes, Chandima & Heidari, Ali Asghar & Chen, Huiling & Ahmadipour, Masoud & Muhsen, Dhiaa Halboot & Alghrairi, Mokhalad, 2021. "Parameters extraction of three diode photovoltaic models using boosted LSHADE algorithm and Newton Raphson method," Energy, Elsevier, vol. 224(C).
    5. Li, Shuijia & Gong, Wenyin & Gu, Qiong, 2021. "A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    6. Zhimin Guo & Zhiyuan Ye & Pengcheng Ni & Can Cao & Xiaozhao Wei & Jian Zhao & Xing He, 2023. "Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System," Energies, MDPI, vol. 16(6), pages 1-21, March.
    7. Samuel R. Fahim & Hany M. Hasanien & Rania A. Turky & Shady H. E. Abdel Aleem & Martin Ćalasan, 2022. "A Comprehensive Review of Photovoltaic Modules Models and Algorithms Used in Parameter Extraction," Energies, MDPI, vol. 15(23), pages 1-56, November.
    8. Gong, Yujian & Wang, Zuo & Lai, Zeyu & Jiang, Minlin, 2021. "TVACPSO-assisted analysis of the effects of temperature and irradiance on the PV module performances," Energy, Elsevier, vol. 227(C).
    9. Muhyaddin Rawa & Abdullah Abusorrah & Yusuf Al-Turki & Martin Calasan & Mihailo Micev & Ziad M. Ali & Saad Mekhilef & Hussain Bassi & Hatem Sindi & Shady H. E. Abdel Aleem, 2022. "Estimation of Parameters of Different Equivalent Circuit Models of Solar Cells and Various Photovoltaic Modules Using Hybrid Variants of Honey Badger Algorithm and Artificial Gorilla Troops Optimizer," Mathematics, MDPI, vol. 10(7), pages 1-31, March.

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