基于差分群时延的物理引导网络同塔双回直流输电线路单端故障定位

吴笑天; 陈仕龙; 杨珠丹; 程 婧; 王 健; 周世燚

doi:10.19783/j.cnki.pspc.260292

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基于差分群时延的物理引导网络同塔双回直流输电线路单端故障定位

应用研究 | 更新时间：2026-10-16

- 基于差分群时延的物理引导网络同塔双回直流输电线路单端故障定位
- Physics‑guided network with differential group delay for single‑ended fault location of tower‑sharing double‑circuit DC transmission lines
- 电力系统保护与控制 2026年54卷第17期
- 作者机构：
  
  昆明理工大学电力工程学院,云南,昆明,650500
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.260292
  中图分类号：
- 纸质出版：2026
- 稿件说明：
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吴笑天, 陈仕龙, 杨珠丹, 等. 基于差分群时延的物理引导网络同塔双回直流输电线路单端故障定位[J]. 电力系统保护与控制, 2026,54(17).

WU Xiaotian, CHEN Shilong, YANG Zhudan, et al. Physics‑guided network with differential group delay for single‑ended fault location of tower‑sharing double‑circuit DC transmission lines[J]. Power System Protection and Control, 2026, 54(17).
吴笑天, 陈仕龙, 杨珠丹, 等. 基于差分群时延的物理引导网络同塔双回直流输电线路单端故障定位[J]. 电力系统保护与控制, 2026,54(17). DOI： 10.19783/j.cnki.pspc.260292.

WU Xiaotian, CHEN Shilong, YANG Zhudan, et al. Physics‑guided network with differential group delay for single‑ended fault location of tower‑sharing double‑circuit DC transmission lines[J]. Power System Protection and Control, 2026, 54(17). DOI： 10.19783/j.cnki.pspc.260292.

摘要

同塔双回直流输电线路导线间电磁耦合关系复杂，准确故障定位存在困难。针对该问题，提出一种基于差分群时延的物理引导网络单端故障定位方案。首先，利用相模变换提取零模与线模分量，并基于行波色散构造频域差分群时延特征，揭示了差分群时延与故障距离间的频率相关映射机制，有效抑制了过渡电阻的干扰。然后，利用边界元件特性，定义边界平滑化指数作为识别判据，实现了区内外故障判别。最后，构建物理引导网络，通过引入物理频率尺度通道、物理调制门控及物理约束学习，将行波传播规律融入网络结构，并结合两阶段测距策略进一步提升了故障测距的精度与稳定性。仿真结果表明，该方案在短数据窗口下定位准确，能够可靠耐受高过渡电阻并抑制强噪声干扰，具有良好的鲁棒性与物理可解释性。

Abstract

The complex electromagnetic coupling between conductors of tower‑sharing double‑circuit HVDC transmission lines poses significant challenges to accurate fault location. To address this issue

a single‑ended fault‑location scheme based on differential group delay and a physics‑guided network is proposed. First

phase‑mode transformation is used to extract the zero‑mode and line‑mode components

and frequency‑domain differential group delay features are constructed based on traveling‑wave dispersion. The frequency‑dependent mapping mechanism between differential group delay and fault distance is thereby revealed

effectively suppressing the interference of transition resistance. Then

based on the characteristics of boundary elements

a boundary smoothing index is defined as the identification criterion to discriminate between internal and external faults. Finally

a physics‑guided network is developed by introducing physical frequency‑scale channels

physics‑based modulation gating

and physics‑constrained learning

thereby embedding traveling‑wave propagation characteristics into the network architecture. Combined with a two‑stage fault‑distance estimation strategy

the accuracy and stability of fault distance estimation are further improved. Simulation results show that the proposed scheme achieves accurate fault location under short data windows

exhibits reliable tolerance to high transition resistance and strong noise interference

and offers good robustness and physical interpretability.

关键词

Keywords

references

LUO J, LIU Y, CUI Q, et al. Single‑ended time domain fault location based on transient signal measurements of transmission lines[J]. Protection and Control of Modern Power Systems, 2024, 9(2): 61‑74.

LI M, NING J, HE J, et al. Initial traveling‑wave‑based single‑ended setting‑less protection for VSC‑MTDC grids[J]. CSEE Journal of Power and Energy Systems, 2026, 12(2): 915‑927.

RADWAN M, AZAD S P. An outlier‑based single‑ended protection scheme for multi‑terminal MMC‑HVDC grids based on Hilbert‑Huang transform[J]. IEEE Transactions on Power Delivery, 2024, 39(6): 3535‑3546.

SHAKIBA F M, AZIZI S M, ZHOU M, et al. Application of machine learning methods in fault detection and classification of power transmission lines: a survey[J]. Artificial Intelligence Review, 2023, 56: 5799‑5836.

ILIYAEIFAR M M, HADAEGHI A. Extreme learning machine‑based fault location approach for terminal‑hybrid LCC‑VSC‑HVDC transmission lines[J]. Electric Power Systems Research, 2023, 221.

YE L, YOU D, YIN X, et al. An improved fault‑location method for distribution system using wavelets and support vector regression[J]. International Journal of Electrical Power Energy Systems, 2014, 55: 467‑472.

HAMIDI S M, AMELI A, GHAFOURI M, et al. A learning‑based framework for locating faults on power grid lines based on distortion of traveling waves[J]. IEEE Transactions on Instrumentation and Measurement, 2022, 71: 1‑15.

REN B, LUO Y J, ZHOU N C, et al. Fault location in high‑voltage direct‑current grids based on panoramic voltage features and vision transformers[J]. International Journal of Electrical Power Energy Systems, 2024, 158.

PARUTHIYIL S K, BIDRAM A, RENO M J. A physics‑informed learning technique for fault location of DC microgrids using traveling waves[J]. IET Generation, Transmission Distribution, 2022, 16(23): 4791‑4805.

XING Y, LIU Y, WANG B, et al. Physics‑informed data‑driven transmission line fault location based on dynamic state estimation[C] // 2022 IEEE Power Energy Society General Meeting (PESGM), July 17‑21, 2022, Denver, USA: 1‑5.

UNNIKRISHAN S, DUTTA R, HE F, et al. On digital twin for multi‑terminal HVDC: application to fault location on the DC side[C] // 2025 AEIT HVDC International Conference (AEIT HVDC), May 29‑30, 2025, Genova, Italy: 1‑6.

HE F, LIU T, TAO D. Why resnet works? Residuals generalize[J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(12): 5349‑5362.

CHICCO D, WARRENS M J, JURMAN G. The coefficient of determination R‑squared is more informative than SMAPE, MAE, MAPE, MSE and RMSE in regression analysis evaluation[J]. Peerj Computer Science, 2021, 7.

CHEN P, XU B, LI J. The optimized combination of fault location technology based on traveling wave principle[C] // 2009 Asia‑Pacific Power and Energy Engineering Conference, March 28‑31, 2009, Wuhan, China: 1‑5.

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