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A method for hybrid extraction of single-diode model parameters of photovoltaics

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  • Arabshahi, M.R.
  • Torkaman, H.
  • Keyhani, A.

Abstract

This paper presents a hybrid method to extract the five unknown parameters of a single-diode photovoltaic (PV) model. The proposed method is a combination of analytical and optimization algorithms such that only two parameters, namely, series (Rs) and shunt resistance (Rsh), are estimated by using metaheuristic algorithms. The information of three major key points in datasheets provided by manufacturers is used for optimization and the sum of the squared error formulated. The rest of the unknown parameters, diode ideality factor (D), photo-generated current (Iph), and dark saturation current (Io) are obtained analytically. In order to predict the behavior of real performance characteristics of solar PV modules under different environmental conditions, a set of translational formulas have been used. Finally, performance indices, such as PV characteristics, absolute error in current, normalized root mean square error (nRMSE), maximum power, and relative maximum power error are estimated for six different types of PV modules from different technology to reveal the effectiveness of the proposed method. Through comparison with experimental data available in modules datasheet and existing method, it was found that the proposed method is sufficiently accurate.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:158:y:2020:i:c:p:236-252
    DOI: 10.1016/j.renene.2020.05.035
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    References listed on IDEAS

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    1. 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.
    2. Dizqah, Arash M. & Maheri, Alireza & Busawon, Krishna, 2014. "An accurate method for the PV model identification based on a genetic algorithm and the interior-point method," Renewable Energy, Elsevier, vol. 72(C), pages 212-222.
    3. Biswas, Partha P. & Suganthan, P.N. & Wu, Guohua & Amaratunga, Gehan A.J., 2019. "Parameter estimation of solar cells using datasheet information with the application of an adaptive differential evolution algorithm," Renewable Energy, Elsevier, vol. 132(C), pages 425-438.
    4. Lineykin, Simon & Averbukh, Moshe & Kuperman, Alon, 2014. "An improved approach to extract the single-diode equivalent circuit parameters of a photovoltaic cell/panel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 282-289.
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    Cited by:

    1. Tifidat, Kawtar & Maouhoub, Noureddine, 2023. "An efficient method for predicting PV modules performance based on the two-diode model and adaptable to the single-diode model," Renewable Energy, Elsevier, vol. 216(C).
    2. Papul Changmai & Sunil Deka & Shashank Kumar & Thanikanti Sudhakar Babu & Belqasem Aljafari & Benedetto Nastasi, 2022. "A Critical Review on the Estimation Techniques of the Solar PV Cell’s Unknown Parameters," Energies, MDPI, vol. 15(19), pages 1-20, September.
    3. 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.
    4. Sairam, Seshapalli & Seshadhri, Subathra & Marafioti, Giancarlo & Srinivasan, Seshadhri & Mathisen, Geir & Bekiroglu, Korkut, 2022. "Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes," Renewable Energy, Elsevier, vol. 185(C), pages 1425-1440.

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