Application of semantic segmentation in High-Impedance fault diagnosis combined signal envelope and Hilbert marginal spectrum for resonant distribution networks

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 1DUNet 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.

Automated summary

This study proposes a novel diagnostic approach for high-impedance faults (HIF) based on the semantic segmentation of the signal envelope (SE) and Hilbert marginal spectrum (HMS). The approach uses 1DUNet to identify the transient process of potential fault events in zero-sequence voltage, and then extracts SE and HMS from longer timescale zero-sequence voltage to represent HIF characteristics. These features are transformed into images, and ResNet18 is employed for HIF detection. An industrial prototype of the proposed approach has been implemented and validated in a 10 kV test system. The experimental results show that the proposed approach outperforms the comparison by a significant margin, especially in resonant distribution networks.