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Temporal graph convolutional autoencoder based fault detection for renewable energy applications.

Arifeen, Murshedul; Petrovski, Andrei

Authors

Andrei Petrovski



Abstract

Detecting faults in energy generation systems is a challenging task due to the complex nature of the system, measurement noise, and outliers. Recently, researchers have shown an increasing interest in using data-driven models that utilize sensor data for fault detection and diagnosis. However, the nonlinearities, spatial and temporal dependencies in timeseries sensor data make it difficult to develop an effective datadriven fault detection model. To address this issue, we propose an autoencoder model that uses a temporal graph convolutional layer to detect faults in the energy generation process. The proposed model has exceptional spatiotemporal feature learning capabilities, making it ideal for fault detection applications. In addition, we have included a data processing module to reduce noise and eliminate outliers from sensor data. We evaluated the model's performance using wind turbine blades and photovoltaic microgrid datasets. Experimental results have demonstrated that the proposed model outperforms other fault detection models based on graph convolutional autoencoders.

Citation

ARIFEEN, M. and PETROVSKI, A. 2024. Temporal graph convolutional autoencoder based fault detection for renewable energy applications. In Proceedings of the 7th IEEE (Institute of Electrical and Electronics Engineers) Industrial cyber-physical systems international conference 2024 (ICPS 2024), 12-15 May 2024, St. Louis, USA. Piscataway: IEEE [online], article number 10639998. Available from: https://doi.org/10.1109/ICPS59941.2024.10639998

Presentation Conference Type Conference Paper (published)
Conference Name 7th IEEE (Institute of Electrical and Electronics Engineers) Industrial cyber-physical systems international conference 2024 (ICPS 2024)
Start Date May 12, 2024
End Date May 15, 2024
Acceptance Date Feb 25, 2024
Online Publication Date Aug 26, 2024
Publication Date Dec 31, 2024
Deposit Date Aug 29, 2024
Publicly Available Date Aug 29, 2024
Publisher Institute of Electrical and Electronics Engineers (IEEE)
Peer Reviewed Peer Reviewed
Series ISSN 2769-3899
DOI https://doi.org/10.1109/icps59941.2024.10639998
Keywords Temporal graph convolutional autoencoder; Fault detection; Wind turbine; Photovoltaic microgrid
Public URL https://rgu-repository.worktribe.com/output/2446269

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ARIFEEN 2024 Temporal graph convolutional (AAM) (438 Kb)
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Publisher Licence URL
https://creativecommons.org/licenses/by/4.0/

Copyright Statement
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.




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