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CPV module electric characterisation by artificial neural networks

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  • García-Domingo, B.
  • Piliougine, M.
  • Elizondo, D.
  • Aguilera, J.

Abstract

Concentrating photovoltaic (CPV) is a relatively new technology with promising future expectations. However, it is at an early stage of development and it has much room for improvement. In order to gain knowledge about CPV technology, outdoor measurements are necessary to adjust models and to study the influence of the atmospheric conditions on the modules performance. In this work, multilayer perceptron models are applied to generate I–V characteristic curves of one of the most extended commercial module of concentrating photovoltaic technology, using the influential atmospheric variables as inputs to the networks. To train these networks an experiment with real measurements was carried out in Jaén, Spain, from July 2011 to June 2012. In addition to a model based on I–V curves expressed as a list of points in Cartesian coordinates, we present an alternative model trained with curves represented in polar coordinates. A previous selection of the most representative samples from the initial dataset was performed using a Kohonen self-organising map. This procedure allows the simulation of the curves even under non-frequent atmospheric conditions. Using the proposed models, it is possible to obtain the characteristic curve of other CPV modules under different meteorological conditions, with high accuracy and fidelity.

Suggested Citation

  • García-Domingo, B. & Piliougine, M. & Elizondo, D. & Aguilera, J., 2015. "CPV module electric characterisation by artificial neural networks," Renewable Energy, Elsevier, vol. 78(C), pages 173-181.
  • Handle: RePEc:eee:renene:v:78:y:2015:i:c:p:173-181
    DOI: 10.1016/j.renene.2014.12.050
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    Cited by:

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    2. 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.
    3. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Real-time prediction intervals for intra-hour DNI forecasts," Renewable Energy, Elsevier, vol. 83(C), pages 234-244.
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    5. Henrik Zsiborács & Attila Bai & József Popp & Zoltán Gabnai & Béla Pályi & István Farkas & Nóra Hegedűsné Baranyai & Mihály Veszelka & László Zentkó & Gábor Pintér, 2018. "Change of Real and Simulated Energy Production of Certain Photovoltaic Technologies in Relation to Orientation, Tilt Angle and Dual-Axis Sun-Tracking. A Case Study in Hungary," Sustainability, MDPI, vol. 10(5), pages 1-19, May.
    6. 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).
    7. Burhan, Muhammad & Chua, Kian Jon Ernest & Ng, Kim Choon, 2016. "Sunlight to hydrogen conversion: Design optimization and energy management of concentrated photovoltaic (CPV-Hydrogen) system using micro genetic algorithm," Energy, Elsevier, vol. 99(C), pages 115-128.
    8. Carlo Renno & Alessandro Perone & Diana D’Agostino & Francesco Minichiello, 2021. "Experimental and Economic Analysis of a Concentrating Photovoltaic System Applied to Users of Increasing Size," Energies, MDPI, vol. 14(16), pages 1-18, August.
    9. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2022. "A network of sky imagers for spatial solar irradiance assessment," Renewable Energy, Elsevier, vol. 187(C), pages 1009-1019.
    10. Carlo Renno, 2021. "Experimental Comparison between Spherical and Refractive Optics in a Concentrating Photovoltaic System," Energies, MDPI, vol. 14(15), pages 1-15, July.

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