Artículo
A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors
Autor/es | Ruiz-Moreno, Sara
Sánchez, Adolfo J. Gallego Len, Antonio Javier Camacho, Eduardo F. |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2022-03 |
Fecha de depósito | 2022-05-31 |
Publicado en |
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Resumen | 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 ... 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%. |
Identificador del proyecto | No 789 051 |
Cita | Ruiz-Moreno, S., Sánchez, A.J., Gallego Len, A.J. y Camacho, E.F. (2022). A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors. Renewable Energy, 186, 691-703. |
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