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

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 Almeria. 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%.(c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study proposes a methodology for detecting and isolating faults in parabolic-trough solar power plants. The methodology consists of three layers, including a neural network for fault detection and classification, analysis of flow rate dynamics, and analysis of thermal losses. The methodology has been applied and achieved high accuracies in fault detection and isolation.