Artículo
A cascade neural network methodology for fault detection and diagnosis in solar thermal plants
Autor/es | Ruiz-Moreno, Sara
![]() ![]() ![]() ![]() ![]() ![]() Gallego Len, Antonio Javier ![]() ![]() ![]() ![]() ![]() ![]() Sánchez, Adolfo J. Camacho, Eduardo F. ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
Departamento | Universidad de Sevilla. Departamento de Ingeniería de Sistemas y Automática |
Fecha de publicación | 2023-07 |
Fecha de depósito | 2023-05-09 |
Publicado en |
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Resumen | 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 ... 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. In this work, real irradiance profiles with many types of clouds are used. First, a model-based fault detector is applied, obtaining an accuracy of over 89% for all test irradiances. Then, different machine learning techniques are compared: static neural networks with and without decoupling strategy, dynamic neural networks, dynamic neural networks in cascade, classification trees, random forests, radial basis function networks, and self-organizing maps. The combination of neural networks was the only method that obtained a total accuracy of over 73% and F1-scores over 50% for all the test irradiance profiles. |
Agencias financiadoras | European Research Council (ERC) Ministerio de Ciencia, Innovación y Universidades (MICINN). España |
Identificador del proyecto | OCONTSOLAR, grant agreement No 789051
![]() FPU20/01958 ![]() |
Cita | Ruiz-Moreno, S., Gallego Len, A.J., Sánchez, A.J. y Camacho, E.F. (2023). A cascade neural network methodology for fault detection and diagnosis in solar thermal plants. Renewable Energy, 211, 76-86. https://doi.org/10.1016/j.renene.2023.04.051. |
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