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针对多分支配电网故障行波定位因线路长度与参数变化导致定位失败的问题，提出一种利用行波主频分量幅值信息的多分支故障定位方法。通过建立频率相关分布参数模型，解析行波主频分量幅频衰减特性，结合S变换提取主频分量幅值，进一步构建行波主频分量幅值与多分支线路的映射关系；给出故障主分支线路T域动态判据，基于行波主频分量幅值传变特性实现最小故障T域的准确辨识，在最小故障T域内通过三端行波到达时间构建故障精确定位模型，消除波速测量误差的影响。仿真结果表明，所提方法能够准确辨识最小故障T域，有效减小线路长度与参数变化的影响，与现有方法相比，检测和定位效果更优良，受装置失效的影响小，有效提高了配电网多分支线路的故障定位精度。
Abstract：Aimed at the failure of traveling-wave-based fault location in multi-branch distribution network caused by variations in line length and parameters,a multi-branch fault location method using the amplitude information of traveling wave dominant frequency components is proposed in this paper. By establishing a frequency-dependent distributed parameter model,the amplitude-frequency attenuation characteristics of dominant frequency components are analyzed.Combined with the S-transform extraction of these component amplitudes,a mapping relationship between the traveling wave dominant frequency component amplitudes and multi-branch lines is further established. A dynamic criterion for identifying the primary branch faulted within the T-domain is put forward,which enables accurate identification of the minimal fault T-domain based on the amplitude propagation characteristics of dominant frequency components. Within this minimal fault T-domain,a precise fault location model is constructed using the arrival time of traveling waves at three terminals,effectively eliminating the influence of wave velocity measurement errors. Simulation results demonstrate that the proposed method can accurately identify the minimal fault T-domain and significantly reduce the influences of variations in line length and parameters. Compared with the existing methods,it exhibits superior detection and location performances,demonstrates minor susceptibility to device failures,and effectively improves the fault location accuracy for multi-branch lines in distribution network.
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基本信息:
DOI：10.19635/j.cnki.csu-epsa.001700
中图分类号:TM73;TP277
引用信息:
[1]黄昕飞,王有鹏,刘凤,等.利用行波主频分量幅值信息的多分支配电网故障定位方法[J].电力系统及其自动化学报,2025,37(12):15-23.DOI:10.19635/j.cnki.csu-epsa.001700.
基金信息:
国家自然科学基金联合基金重点支持项目（U22B20113）
2025-06-17
2025
2025-12-26
2025
1
2025-08-07
2025-08-07
2025-08-07