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Deep Learning-Based Fault Detection and Isolation in Solar Plants for Highly Dynamic Days
(IEEE, 2022)
Solar plants are exposed to numerous agents that degrade and damage their components. Due to their large size and constant operation, it is not easy to access them constantly to analyze possible failures on-site. It is, ...
Artículo
A cascade neural network methodology for fault detection and diagnosis in solar thermal plants
(Elsevier, 2023-07)
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 ...
Artículo
A deep learning-based strategy for fault detection and isolation in parabolic-trough collectors
(Elsevier, 2022-03)
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 ...
Artículo
Model predictive control based on deep learning for solar parabolic-trough plants
(Elsevier, 2021-12)
In solar parabolic-trough plants, the use of Model Predictive Control (MPC) increases the output thermal power. However, MPC has the disadvantage of a high computational demand that hinders its application to some processes. ...