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针对配电网行波波头标定方法易受噪声、波头畸变影响的问题，提出融合时变滤波经验模态分解与熵峭比的行波波头标定法。首先分析行波信号在长短分支线路的传播过程，揭示分支线路与行波波头畸变之间的内在联系，进而通过时变滤波经验模态分解行波信号得到多个固有模态分量，有效抑制模态混叠并保留高频波头特征，并引入熵峭比选取有效的模态分量，在此基础上通过Teager能量算子对有效模态分量进行波头标定，得到行波首波头到达检测端的精确时刻。仿真结果表明，相较于现有方法，该方法不仅显著提升了行波波头标定精度，更适用于畸变行波信号，同时验证了其具有较强的抗噪能力，能够提升配电网故障行波定位精度，为复杂配电网故障定位提供了可靠技术支撑。
Abstract：To address the susceptibility of traveling wave wavefront calibration methods for distribution networks to noise interference and wavefront distortion,a traveling wave wavefront calibration method integrating time-varying filtering-based empirical mode decomposition(TVFEMD)and entropy-kurtosis ratio is proposed in this paper. First,the propagation process of traveling wave signals in long and short branch lines is analyzed,revealing the intrinsic link between branch lines and traveling wave wavefront distortion. Second,the traveling wave signals are decomposed into multiple intrinsic mode function(IMF)components via TVFEMD,which effectively suppresses mode mixing while preserving the high-frequency wavefront features. In addition,an entropy-kurtosis ratio is introduced to select the effective IMF component. Subsequently,the Teager energy operator is applied to calibrate the wavefront for the effective IMF component,thus obtaining the precise arrival time of the initial wavefront at the detection terminal. Simulation results demonstrate that compared with the existing methods,the proposed approach not only significantly enhances the wavefront calibration accuracy,but also excels in processing the distorted traveling wave signals. Meanwhile,its strong noise immunity is verified,indicating that it can improve the fault location precision in distribution networks. The results in this paper provide reliable technical support for the location of complex distribution network faults.
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基本信息:
DOI：10.19635/j.cnki.csu-epsa.001689
中图分类号:TM75;TN713
引用信息:
[1]黄昕飞,刘凤,邵杰,等.融合时变滤波经验模态分解与熵峭比的行波波头标定法[J].电力系统及其自动化学报,2026,38(03):12-23.DOI:10.19635/j.cnki.csu-epsa.001689.
基金信息:
国家自然科学基金联合基金重点支持项目（U22B20113）; 南方电网公司数字研究院有限公司科技项目（210002KK52222011）