电力系统保护与控制2026，Vol.54Issue(17)：60-71,12.DOI:10.19783/j.cnki.pspc.260292
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
摘要
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
单端故障定位/同塔双回/色散特征/群时延/深度学习Key words
single-ended fault location/tower-sharing double-circuit transmission lines/dispersion characteristics/group delay/deep learning引用本文复制引用
吴笑天,陈仕龙,杨珠丹,程婧,王健,周世燚..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):60-71,12.基金项目
This work is supported by the National Natural Science Foundation of China(No.52067009). 国家自然科学基金项目资助(52067009) （No.52067009）
