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

Arifeen, Murshedul; Petrovski, Andrei

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