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Multi-junction solar cells electrical characterization by neuronal networks under different irradiance, spectrum and cell temperature

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  • Fernández, Eduardo F.
  • Almonacid, Florencia
  • Garcia-Loureiro, Antonio J.

Abstract

Nowadays, HCPV (high concentrator photovoltaics) is largely based on high efficiency MJ (multi-junction) solar cells. Hence, the prediction of the electrical parameters of MJ solar cells is crucial for designing and evaluating the performance of this emerging technology. At the same time, the analytical modelling of the I–V parameters of these devices is complex due to their strong and complex dependence with irradiance, spectrum and cell temperature. In this work, the possibility of predicting the main electrical characteristics of a MJ solar cell by using artificial intelligent techniques is analysed. In particular, three artificial neural network (ANN)-based models were developed: one for simulating the short-circuit current (Isc), one for simulating the open-circuit voltage (Voc) and for simulating the maximum power (Pmax). The models were developed and evaluated with the data of a lattice-matched GaInP/GaInAs/Ge triple-junction operating at a wide range of conditions. Results show that the models accurately estimate the main electrical parameters of a MJ solar cell under different concentrated sunlight, spectral irradiance and cell temperature with a RMSE (root mean square error) lower than 0.5% and a MBE (mean bias error) almost 0%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:energy:v:90:y:2015:i:p1:p:846-856
    DOI: 10.1016/j.energy.2015.07.123
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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Talavera, D.L. & Pérez-Higueras, P. & Almonacid, F. & Fernández, E.F., 2017. "A worldwide assessment of economic feasibility of HCPV power plants: Profitability and competitiveness," Energy, Elsevier, vol. 119(C), pages 408-424.
    5. Fernandez, Eduardo F. & Chemisana, Daniel & Micheli, Leonardo & Almonacid, Florencia, 2019. "Spectral nature of soiling and its impact on multi-junction based concentrator systems," MPRA Paper 106251, University Library of Munich, Germany.
    6. Wu, Chen-Wu & Peng, Qing & Huang, Chen-Guang, 2017. "Thermal analysis on multijunction photovoltaic cell under oblique incident laser irradiation," Energy, Elsevier, vol. 134(C), pages 248-255.
    7. Saura, José M. & Chemisana, Daniel & Rodrigo, Pedro M. & Almonacid, Florencia M. & Fernández, Eduardo F., 2022. "Effect of non-uniformity on concentrator multi-junction solar cells equipped with refractive secondary optics under shading conditions," Energy, Elsevier, vol. 238(PC).

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