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A cascade neural network methodology for fault detection and diagnosis in solar thermal plants

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  • Ruiz-Moreno, Sara
  • Gallego, Antonio J.
  • Sanchez, Adolfo J.
  • Camacho, Eduardo F.

Abstract

Detecting and isolating faults in collector fields of solar thermal power plants is a crucial and challenging task. The system variables in the collector area are highly coupled, which can lead to a high misclassification rate. For this reason, it becomes necessary to combine knowledge of systems engineering with machine learning techniques that unravel the complex dynamics that govern the systems using historical data. Furthermore, the performance of a solar thermal plant is highly dependent on solar irradiance which changes during the day and is subject to perturbations caused by clouds and other atmospheric conditions. Detecting the fault requires using techniques that cope with the disturbances in solar irradiance.

Suggested Citation

  • Ruiz-Moreno, Sara & Gallego, Antonio J. & Sanchez, Adolfo J. & Camacho, Eduardo F., 2023. "A cascade neural network methodology for fault detection and diagnosis in solar thermal plants," Renewable Energy, Elsevier, vol. 211(C), pages 76-86.
    • Handle: RePEc:eee:renene:v:211:y:2023:i:c:p:76-86
    DOI: 10.1016/j.renene.2023.04.051
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    References listed on IDEAS

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    1. Correa-Jullian, Camila & Cardemil, José Miguel & López Droguett, Enrique & Behzad, Masoud, 2020. "Assessment of Deep Learning techniques for Prognosis of solar thermal systems," Renewable Energy, Elsevier, vol. 145(C), pages 2178-2191.
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    4. Ruiz-Moreno, Sara & Sanchez, Adolfo J. & Gallego, Antonio J. & Camacho, Eduardo F., 2022. "A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors," Renewable Energy, Elsevier, vol. 186(C), pages 691-703.
    5. Ajbar, Wassila & Parrales, A. & Huicochea, A. & Hernández, J.A., 2022. "Different ways to improve parabolic trough solar collectors’ performance over the last four decades and their applications: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
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    Cited by:

    1. Yousri, Dalia & Babu, Thanikanti Sudhakar & Pachauri, Rupendra Kumar & Zeineldin, Hatem & El-Saadany, Ehab F., 2024. "A novel argyle puzzle for partial shading effect mitigation with experimental validation," Renewable Energy, Elsevier, vol. 225(C).

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