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Anomaly detection for solar thermal parabolic trough power plants with artificial intelligence

Braun, Josua (2020) Anomaly detection for solar thermal parabolic trough power plants with artificial intelligence. Master's, University of Applied Sciences Esslingen.

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Abstract

Defects and faults in parabolic trough power plants often lead to lower energy production. The automated detection of such anomalies could reduce downtimes and increase efficiency. The anomaly detection is based on sensor data which are measured anyway for the operation of the plant. The measured data are high-dimensional data in a spatio-temporal context. In the first step, the efficient preprocessing and visualization of the solar field and weather data is realized. From the existing sensor data useful features are extracted, which build the input for further approaches of anomaly detection models. Regarding the given problem, approaches that make use of methods of artificial intelligence are presented. One approach is pursued. The basic idea of this approach is the consideration of multivariate time series on loop level, which are segmented and clustered. For the segmentation of the time series three different segmentation methods based on the bottom-up or sliding-window principle are compared. For clustering, the density-based DBSCAN algorithm is used. The results of the anomaly detection show that about 93 % of the detected time series segments behave unusually from the data point of view due to manual interventions in the operation of the power plant. Suggestions for an improvement are given to reduce the number of false detections. Furthermore, weaknesses of the implemented approach are pointed out.

Item URL in elib:https://elib.dlr.de/139874/
Document Type:Thesis (Master's)
Title:Anomaly detection for solar thermal parabolic trough power plants with artificial intelligence
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Braun, JosuaJosua.Braun (at) dlr.deUNSPECIFIED
Date:12 December 2020
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Number of Pages:90
Status:Published
Keywords:Anomaly detection, parabolic trough, python, clustering
Institution:University of Applied Sciences Esslingen
HGF - Research field:Energy
HGF - Program:Renewable Energies
HGF - Program Themes:Concentrating Solar Thermal Technology
DLR - Research area:Energy
DLR - Program:E SW - Solar and Wind Energy
DLR - Research theme (Project):E - Advanced Heat Transfer Media (old)
Location: Stuttgart
Institutes and Institutions:Institute of Solar Research > Solar High Temperature Technologies
Deposited By: Brenner, Alex
Deposited On:23 Dec 2020 14:56
Last Modified:23 Dec 2020 14:56

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