Application of Semantic Segmentation in High-Impedance Fault Diagnosis Combined Signal Envelope and Hilbert Marginal Spectrum for Resonant Distribution Networks

Gao, Jian-Hong; Guo, Mou-Fa; Lin, Shuyue; Chen, Duan-Yu

Abstract

The diagnosis of high-impedance fault (HIF) is a critical challenge due to the presence of faint signals that exhibit distortion and randomness. In this study, we propose a novel diagnostic approach for HIF based on semantic segmentation of the signal envelope (SE) and Hilbert marginal spectrum (HMS). The proposed approach uses 1D-UNet to identify the transient process of potential fault events in zero-sequence voltage to judge fault inception. Longer timescale zero-sequence voltage is then used to extract SE and HMS, representing HIF distortion and randomness characteristics. These features are transformed into images, and ResNet18 is employed to detect the presence of HIF. An industrial prototype of the proposed approach has been implemented and validated in a 10 kV test system. The experimental results indicate that the proposed approach outperforms the comparison by a significant margin regarding triggering deviation and detection accuracy, particularly in resonant distribution networks.

Citation

Gao, J.-H., Guo, M.-F., Lin, S., & Chen, D.-Y. (2023). Application of Semantic Segmentation in High-Impedance Fault Diagnosis Combined Signal Envelope and Hilbert Marginal Spectrum for Resonant Distribution Networks. Expert Systems with Applications, 231, Article 120631. https://doi.org/10.1016/j.eswa.2023.120631

• Journal Article Type: Article
• Acceptance Date: May 28, 2023
• Online Publication Date: Jun 1, 2023
• Publication Date: Nov 30, 2023
• Deposit Date: Jun 14, 2023
• Publicly Available Date: Jun 2, 2024
• Journal: Expert systems with applications
• Print ISSN: 0957-4174
• Electronic ISSN: 0957-4174
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 231
• Article Number: 120631
• DOI: https://doi.org/10.1016/j.eswa.2023.120631
• Public URL: https://hull-repository.worktribe.com/output/4301880