Wavefront-Based Detection of Single Line-to-Ground Fault Echoes in Distribution Networks with Multi-Mechanism Fusion

This paper proposes a wavefront-based method for detecting and locating single-line-to-ground faults in distribution lines using only the transient waveform recorded at one line terminal. The measured current is transformed into a time–frequency representation by the S-transform, and a low-rank structure is extracted by truncated singular value decomposition to suppress broadband noise. On this basis, a hysteresis-type energy envelope is constructed to determine the onset of the fault surge front. To distinguish the genuine fault echo—the main reflection associated with the fault location—from branch echoes and terminal ringing, three complementary criteria are combined: a generalized likelihood ratio test on the time–frequency energy, a dual-pulse interval matching based on the expected round-trip time between the terminal and the fault, and a multi-band consistency check over low-, medium-, and high-frequency components. Numerical experiments under different fault locations and signal-to-noise ratios show that the proposed method improves the average echo recognition rate by about 3.5% compared with conventional single-criterion detectors, while maintaining accurate wavefront-onset estimation with MHz-class sampling (1–5 MHz) that is readily available in practical on-line travelling-wave recorders, rather than relying on ultra-high sampling (e.g., tens of MHz and above). The method therefore offers a physically interpretable and practically feasible tool for fault-echo detection in overhead distribution feeders.

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Publication Details

Journal
Energies
Published
2026-01-20
DOI
https://doi.org/10.3390/en19020510
Primary Topic
Power Systems Fault Detection
Type
article
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article

Wavefront-Based Detection of Single Line-to-Ground Fault Echoes in Distribution Networks with Multi-Mechanism Fusion

Penghui Chang, L Zhang, Tengjiao Li, Weiqing Sun
Energies
Power Systems Fault Detection
article

Wavefront-Based Detection of Single Line-to-Ground Fault Echoes in Distribution Networks with Multi-Mechanism Fusion

Penghui Chang, L Zhang, Tengjiao Li, Weiqing Sun
article en

Abstract

This paper proposes a wavefront-based method for detecting and locating single-line-to-ground faults in distribution lines using only the transient waveform recorded at one line terminal. The measured current is transformed into a time–frequency representation by the S-transform, and a low-rank structure is extracted by truncated singular value decomposition to suppress broadband noise. On this basis, a hysteresis-type energy envelope is constructed to determine the onset of the fault surge front. To distinguish the genuine fault echo—the main reflection associated with the fault location—from branch echoes and terminal ringing, three complementary criteria are combined: a generalized likelihood ratio test on the time–frequency energy, a dual-pulse interval matching based on the expected round-trip time between the terminal and the fault, and a multi-band consistency check over low-, medium-, and high-frequency components. Numerical experiments under different fault locations and signal-to-noise ratios show that the proposed method improves the average echo recognition rate by about 3.5% compared with conventional single-criterion detectors, while maintaining accurate wavefront-onset estimation with MHz-class sampling (1–5 MHz) that is readily available in practical on-line travelling-wave recorders, rather than relying on ultra-high sampling (e.g., tens of MHz and above). The method therefore offers a physically interpretable and practically feasible tool for fault-echo detection in overhead distribution feeders.

EnergiesVol. 19(2)
University of Shanghai for Science and Technology (CN), Luohe Medical College (CN), Shanghai Electric (China) (CN)
Openalex Percentile: Top 83%
Power Systems Fault Detection
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Wavefront-Based Detection of Single Line-to-Ground Fault Echoes in Distribution Networks with Multi-Mechanism Fusion — Penghui Chang, L Zhang, et al. · Energies (2026) | TGRS Research Map | TGRS