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A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors

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

Abstract

Solar plants are exposed to the appearance of faults in some of their components, as they are vulnerable to the action of external agents (wind, rain, dust, birds …) and internal defects. However, it is necessary to ensure a satisfactory operation when these factors affect the plant. Fault detection and diagnosis methods are essential to detecting and locating the faults, maintaining efficiency and safety in the plant. This work proposes a methodology for detecting and isolating faults in parabolic-trough plants. It is based on a three-layer methodology composed of a neural network to obtain a preliminary detection and classification between three types of fault, a second stage analyzing the flow rate dynamics, and a third stage defocusing the first collector to analyze thermal losses. The methodology has been applied by simulation to a model of the ACUREX plant, which was located at the Plataforma Solar de Almería. The confusion matrices have been obtained, with accuracies over 80% when using the three layers in a hierarchical structure. By forcing all the three layers, the accuracies exceed 90%.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:renene:v:186:y:2022:i:c:p:691-703
    DOI: 10.1016/j.renene.2022.01.029
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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.
    2. Hussain, Muhammed & Dhimish, Mahmoud & Titarenko, Sofya & Mather, Peter, 2020. "Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters," Renewable Energy, Elsevier, vol. 155(C), pages 1272-1292.
    3. Ruiming, Fang & Minling, Wu & xinhua, Guo & Rongyan, Shang & Pengfei, Shao, 2020. "Identifying early defects of wind turbine based on SCADA data and dynamical network marker," Renewable Energy, Elsevier, vol. 154(C), pages 625-635.
    4. Islam, Md Tasbirul & Huda, Nazmul & Abdullah, A.B. & Saidur, R., 2018. "A comprehensive review of state-of-the-art concentrating solar power (CSP) technologies: Current status and research trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 987-1018.
    5. Antonio J. Gallego & Manuel Macías & Fernando de Castilla & Eduardo F. Camacho, 2019. "Mathematical Modeling of the Mojave Solar Plants," Energies, MDPI, vol. 12(21), pages 1-20, November.
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    1. 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.

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