电力系统及其自动化学报2026，Vol.38Issue(3)：24-35,12.DOI:10.19635/j.cnki.csu-epsa.001671
时变滤波互补集合经验模态分解和STET的配电网故障定位方法
Distribution Network Fault Location Method Based on Time-varying Filtering Complementary Ensemble Empirical Mode Decomposition and STET
摘要
Abstract
In response to the problem of inaccurate fault location caused by the difficulty in wave head extraction for the distribution network fault traveling wave in an environment with strong noise,a distribution network fault location meth⁃od based on time-varying filtering complementary ensemble empirical mode decomposition and second-order transient extraction transform(STET)is proposed.First,the traveling wave signal containing strong noise is decomposed using time-varying filtering complementary ensemble empirical mode decomposition to obtain each intrinsic mode function,and the effective signal is selected and reconstructed by calculating the corresponding energy entropy increment to ob⁃tain the noise-reduced traveling wave signal.Second,the improved complete ensemble empirical mode decomposition algorithm with adaptive noise is used to decompose the noise-reduced signal,so as to extract the intrinsic mode func⁃tions containing the fault information.Finally,STET is performed on the extracted components to highlight the traveling wave mutation characteristics,thus realizing the accurate extraction of wave arrival time.In addition,the improved dou⁃ble-terminal method is used to realize the precise fault location.Through simulations,it is verified that the fault location error of the proposed method in the environment with strong noise is less than 1%,and good applicability and location accuracy are maintained under conditions of multiple fault types.关键词
行波/故障定位/时变滤波互补集合经验模态分解/二阶瞬态提取变换/能量熵增量Key words
traveling wave/fault location/time-varying filtering complementary ensemble empirical mode decomposi⁃tion/second-order transient extraction transform(STET)/energy entropy increment分类
信息技术与安全科学引用本文复制引用
郭成,李晓明,何觅..时变滤波互补集合经验模态分解和STET的配电网故障定位方法[J].电力系统及其自动化学报,2026,38(3):24-35,12.基金项目
国家自然科学基金资助项目(52367002) （52367002）
云南省自然科学基金资助项目(202201BE070001-15). （202201BE070001-15）
