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An accurate modelling of Photovoltaic modules based on two-diode model

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  • Chennoufi, Khalid
  • Ferfra, Mohammed
  • Mokhlis, Mohcine

Abstract

The photovoltaic module is represented by an equivalent electrical circuit with five or seven parameters depending on the complexity of the model, these parameters are unknown and crucial to predict the performance of a PV module. In this paper, in order to extract the seven parameters of double diode model a hybrid method, which combines analytical and numerical methods, is proposed. The analytic modelling is obtained by combining the equations of three characteristic points which are the open circuit voltage, the short circuit current and the maximum power point. While the numerical method is employed to find the value of series resistance and of the ideality factors in which the computed and the datasheet powers are equal, the other parameters are computed afterward using analytical equations. The validity of the proposed method for various PV modules has been analysed under different temperature and irradiance conditions and the obtained curves were compared with experimental data. The results confirm the advantages of the hybrid method, furthermore, the absolute error and the RMSE affirmed the accuracy of the proposed approach.

Suggested Citation

  • Chennoufi, Khalid & Ferfra, Mohammed & Mokhlis, Mohcine, 2021. "An accurate modelling of Photovoltaic modules based on two-diode model," Renewable Energy, Elsevier, vol. 167(C), pages 294-305.
    • Handle: RePEc:eee:renene:v:167:y:2021:i:c:p:294-305
    DOI: 10.1016/j.renene.2020.11.085
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    References listed on IDEAS

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    1. Oliva, Diego & Abd El Aziz, Mohamed & Ella Hassanien, Aboul, 2017. "Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm," Applied Energy, Elsevier, vol. 200(C), pages 141-154.
    2. Bana, Sangram & Saini, R.P., 2017. "Identification of unknown parameters of a single diode photovoltaic model using particle swarm optimization with binary constraints," Renewable Energy, Elsevier, vol. 101(C), pages 1299-1310.
    3. Orioli, Aldo & Di Gangi, Alessandra, 2019. "A procedure to evaluate the seven parameters of the two-diode model for photovoltaic modules," Renewable Energy, Elsevier, vol. 139(C), pages 582-599.
    4. 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.
    5. Chen, Xu & Yu, Kunjie & Du, Wenli & Zhao, Wenxiang & Liu, Guohai, 2016. "Parameters identification of solar cell models using generalized oppositional teaching learning based optimization," Energy, Elsevier, vol. 99(C), pages 170-180.
    6. Arabshahi, M.R. & Torkaman, H. & Keyhani, A., 2020. "A method for hybrid extraction of single-diode model parameters of photovoltaics," Renewable Energy, Elsevier, vol. 158(C), pages 236-252.
    7. Huawen Sheng & Chunquan Li & Hanming Wang & Zeyuan Yan & Yin Xiong & Zhenting Cao & Qianying Kuang, 2019. "Parameters Extraction of Photovoltaic Models Using an Improved Moth-Flame Optimization," Energies, MDPI, vol. 12(18), pages 1-23, September.
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    Cited by:

    1. Li, Fuxiang & Wu, Wei, 2022. "Coupled electrical-thermal performance estimation of photovoltaic devices: A transient multiphysics framework with robust parameter extraction and 3-D thermal analysis," Applied Energy, Elsevier, vol. 319(C).
    2. Radouane Aalloul & Abdellah Elaissaoui & Mourad Benlattar & Rhma Adhiri, 2023. "Emerging Parameters Extraction Method of PV Modules Based on the Survival Strategies of Flying Foxes Optimization (FFO)," Energies, MDPI, vol. 16(8), pages 1-24, April.
    3. Li, Guorong & Zhang, Yunpeng & Zhou, Hai & Wu, Ji & Sun, Shumin & You, Daning & Zhang, Yuanpeng, 2024. "Novel reference condition independent method for estimating performance for PV modules based on double-diode model," Renewable Energy, Elsevier, vol. 226(C).
    4. Kumar, Manish & Malik, Prashant & Chandel, Rahul & Chandel, Shyam Singh, 2023. "Development of a novel solar PV module model for reliable power prediction under real outdoor conditions," Renewable Energy, Elsevier, vol. 217(C).

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