Artificial generation of partial discharge sources through an algorithm based on deep convolutional generative adversarial networks.

Ardila-Rey, Jorge Alfredo; Ortiz, Jesus Eduardo; Creixell, Werner; Muhammad-Sukki, Firdaus; Bani, Nurul Aini

Abstract

The measurement of partial discharges (PD) in electrical equipment or machines subjected to high voltage can be considered as one of the most important indicators when assessing the state of an insulation system. One of the main challenges in monitoring these degradation phenomena is to adequately measure a statistically significant number of signals from each of the sources acting on the asset under test. However, in industrial environments the presence of large amplitude noise sources or the simultaneous presence of multiple PD sources may limit the acquisition of the signals and therefore the final diagnosis of the equipment status may not be the most accurate. Although different procedures and separation and identification techniques have been implemented with very good results, not having a significant number of PD pulses associated with each source can limit the effectiveness of these procedures. Based on the above, this research proposes a new algorithm of artificial generation of PD based on a Deep Convolutional Generative Adversarial Networks (DCGAN) architecture which allows artificially generating different sources of PD from a small group of real PD, in order to complement those sources that during the measurement were poorly represented in terms of signals. According to the results obtained in different experiments, the temporal and spectral behavior of artificially generated PD sources proved to be similar to that of real experimentally obtained sources.

Citation

ARDILA-REY, J.A., ORTIZ, J.E., CREIXELL, W., MUHAMMAD-SUKKI, F. and BANI, N.A. 2020. Artificial generation of partial discharge sources through an algorithm based on deep convolutional generative adversarial networks. IEEE access [online], 8, pages 24561-24575. Available from: https://doi.org/10.1109/ACCESS.2020.2971319

• Journal Article Type: Article
• Acceptance Date: Jan 17, 2020
• Online Publication Date: Feb 3, 2020
• Publication Date: Feb 10, 2020
• Deposit Date: Feb 5, 2020
• Publicly Available Date: Feb 5, 2020
• Journal: IEEE Access
• Electronic ISSN: 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers (IEEE)
• Peer Reviewed: Peer Reviewed
• Volume: 8
• Pages: 24561-24575
• DOI: https://doi.org/10.1109/ACCESS.2020.2971319
• Keywords: Partial discharge; Electrical noise sources; Machine learning; Spectral power clustering technique; Clustering
• Public URL: https://rgu-repository.worktribe.com/output/844703