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Parameters extraction of single diode model for degraded photovoltaic modules

Author

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  • Piliougine, M.
  • Guejia-Burbano, R.A.
  • Petrone, G.
  • Sánchez-Pacheco, F.J.
  • Mora-López, L.
  • Sidrach-de-Cardona, M.

Abstract

The single–diode model is widely used for the analysis of photovoltaic systems and reproducing accurately the I–V curve. Numerical or analytical methods can be employed to estimate the model parameters; among them explicit methods are well assessed providing precise results and low computational complexity, thus suitable to be developed on embedded systems. Due to their approximated nature, the accuracy of such methods may be affected by the operating conditions and by the state of health of the photovoltaic modules that have been characterised. The main contribution of this paper is to analyse a selection of explicit methods with the aim of testing their capability to detect degradation in photovoltaic modules. Since different degradation phenomena are reflected in a variation of the series resistance of the single diode equivalent circuit, the study is mainly focused on the estimation of this parameter. The comparison of different explicit methods has been done by using outdoor experimental I–V curves of a photovoltaic module operating in normal as well as degraded conditions. The analysis shows that only few methods exhibit enough reliability to estimate correctly the model parameters in presence of degradation and are less sensible to the environmental operating conditions.

Suggested Citation

  • Piliougine, M. & Guejia-Burbano, R.A. & Petrone, G. & Sánchez-Pacheco, F.J. & Mora-López, L. & Sidrach-de-Cardona, M., 2021. "Parameters extraction of single diode model for degraded photovoltaic modules," Renewable Energy, Elsevier, vol. 164(C), pages 674-686.
    • Handle: RePEc:eee:renene:v:164:y:2021:i:c:p:674-686
    DOI: 10.1016/j.renene.2020.09.035
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    2. Jingwei Zhang & Zenan Yang & Kun Ding & Li Feng & Frank Hamelmann & Xihui Chen & Yongjie Liu & Ling Chen, 2022. "Modeling of Photovoltaic Array Based on Multi-Agent Deep Reinforcement Learning Using Residuals of I–V Characteristics," Energies, MDPI, vol. 15(18), pages 1-17, September.
    3. 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.
    4. Piliougine, Michel & Sánchez-Friera, Paula & Petrone, Giovanni & Sánchez-Pacheco, Francisco José & Spagnuolo, Giovanni & Sidrach-de-Cardona, Mariano, 2022. "New model to study the outdoor degradation of thin–film photovoltaic modules," Renewable Energy, Elsevier, vol. 193(C), pages 857-869.
    5. Andrés Firman & Cesar Prieb & Alexis Raúl González Mayans & Manuel Cáceres & Luis Horacio Vera & Juan de la Casa Higueras, 2023. "New Approach for Photovoltaic Parameters Extraction for Low-Cost Electronic Devices," Energies, MDPI, vol. 16(13), pages 1-13, June.

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