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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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    Cited by:

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