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High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature

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  • Almonacid, F.
  • Fernández, E.F.
  • Mallick, T.K.
  • Pérez-Higueras, P.J.

Abstract

The electrical modelling of HCPV (high concentrator photovoltaic) modules is a key issue for systems design and energy prediction. However, the electrical modelling of HCPV modules shows a significantly level of complexity than conventional photovoltaic technology because of the use of multi-junction solar cells and optical devices. In this paper, a method for the simulation of the I–V curves of a HCPV module at any operating condition is introduced. The method is based on three different ANN (artificial neural networks)-based models: one to spectrally correct the direct normal irradiance, one to predict the cell temperature and one to generate the I–V curve of the HCPV module. The method has the advantage that is fully based on atmospheric parameter and outdoor measurements. The analysis of results shows that the method accurately predicts the I–V curve of a HCPV module for a wide range of atmospheric operating conditions with a RMSE (root mean square error) ranging from 0.19% to 1.66% and a MBE (mean bias error) ranging from −0.38% to 0.40%.

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  • Almonacid, F. & Fernández, E.F. & Mallick, T.K. & Pérez-Higueras, P.J., 2015. "High concentrator photovoltaic module simulation by neuronal networks using spectrally corrected direct normal irradiance and cell temperature," Energy, Elsevier, vol. 84(C), pages 336-343.
    • Handle: RePEc:eee:energy:v:84:y:2015:i:c:p:336-343
    DOI: 10.1016/j.energy.2015.02.105
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    Cited by:

    1. Said, Mohamed Islam & Steiner, Marc & Siefer, Gerald & Arab, Amar Hadj, 2020. "Maximum power output prediction of HCPV FLATCON® module using an ANN approach," Renewable Energy, Elsevier, vol. 152(C), pages 1274-1283.
    2. Manuel Angel Gadeo-Martos & Antonio Jesús Yuste-Delgado & Florencia Almonacid Cruz & Jose-Angel Fernandez-Prieto & Joaquin Canada-Bago, 2019. "Modeling a High Concentrator Photovoltaic Module Using Fuzzy Rule-Based Systems," Energies, MDPI, vol. 12(3), pages 1-22, February.
    3. Almonacid, Florencia & Rodrigo, Pedro & Fernández, Eduardo F., 2016. "Determination of the current–voltage characteristics of concentrator systems by using different adapted conventional techniques," Energy, Elsevier, vol. 101(C), pages 146-160.
    4. Almonacid, Florencia & Fernandez, Eduardo F. & Mellit, Adel & Kalogirou, Soteris, 2017. "Review of techniques based on artificial neural networks for the electrical characterization of concentrator photovoltaic technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 938-953.
    5. Chen, Zhicong & Yu, Hui & Luo, Linlu & Wu, Lijun & Zheng, Qiao & Wu, Zhenhui & Cheng, Shuying & Lin, Peijie, 2021. "Rapid and accurate modeling of PV modules based on extreme learning machine and large datasets of I-V curves," Applied Energy, Elsevier, vol. 292(C).
    6. Fernández, Eduardo F. & Almonacid, Florencia & Garcia-Loureiro, Antonio J., 2015. "Multi-junction solar cells electrical characterization by neuronal networks under different irradiance, spectrum and cell temperature," Energy, Elsevier, vol. 90(P1), pages 846-856.
    7. Manuel Cáceres & Andrés Firman & Jesús Montes-Romero & Alexis Raúl González Mayans & Luis Horacio Vera & Eduardo F. Fernández & Juan de la Casa Higueras, 2020. "Low-Cost I–V Tracer for PV Modules under Real Operating Conditions," Energies, MDPI, vol. 13(17), pages 1-17, August.

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