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基于信号频谱特性的配电网故障行波检测方法OA北大核心CSTPCD

A fault traveling wave detection method based on signal spectral characteristics for a distribution network

中文摘要英文摘要

针对配电网干扰情况下微弱故障信号特征不明显导致行波采集设备难以有效检测故障行波信号的问题,提出一种基于信号频谱特性的配电网故障行波检测方法.首先,通过分析配电网故障行波的传输特征与频率特性,建立基于波形增量比值的启动判据,对设备采样数据进行预处理,减少行波定位装置的误启动.然后,引入鲁棒性局部均值分解(robust local mean decomposition,RLMD)方法处理采样数据,滤除采样过程中的干扰信号,减少噪声信号的影响.最后,根据行波低频含量衰减较小而高频含量衰减快的性质,建立故障行波辨识判据,辨识配电网故障行波信号.仿真表明,所提方法能够有效检测微弱故障时的行波信号.

There is a problem in that the characteristics of weak fault signals are not obvious when there is interference on the distribution network.This makes it difficult for traveling wave acquisition equipment to effectively detect the fault traveling wave signals.Thus a method of fault traveling wave detection based on signal spectral characteristics is proposed.By analyzing the transmission characteristics and frequency characteristics of the fault traveling wave in distribution networks,a start-up criterion is established based on the waveform incremental ratio.It can preprocess the sampling data of the equipment,and reduce the false start-up of the traveling wave equipment.Then,the robust local mean decomposition(RLMD)method is used to process the sampling data,filter out the interference signal during the sampling process,and reduce the influence of the noise signal.Finally,given that it is a characteristic of the traveling wave that the low-frequency content attenuates less but the high-frequency content attenuates faster,an identification criterion is established to identify the fault traveling wave signals.Simulations show that the proposed method can effectively detect the fault traveling wave signals during weak faults.

刘丰;谢李为;蔡军;喻锟;王有鹏;曾祥君;唐欣

长沙理工大学电气与信息工程学院,湖南 长沙 410114国网湖南省电力有限公司长沙供电分公司,湖南 长沙 410015

配电网故障行波检测RLMD多分支线路

distribution networkfault traveling wave detectionRLMDmulti-branches

《电力系统保护与控制》 2024 (009)

59-69 / 11

This work is supported by the National Natural Science Foundation of China(No.U22B20113). 国家自然科学基金项目资助(U22B20113);湖南省自然科学基金项目资助(2021JJ30729)

10.19783/j.cnki.pspc.231451

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