李峰, 徐丙垠, 宫士营. 基于EMD的电弧反射电缆故障测距脉冲信号提取方法[J]. 电力系统自动化, 2012, 36(11): 92-96.
引用本文: 李峰, 徐丙垠, 宫士营. 基于EMD的电弧反射电缆故障测距脉冲信号提取方法[J]. 电力系统自动化, 2012, 36(11): 92-96.
LI Feng, XU Bing-yin, GONG Shi-ying. Extraction of Pulse Signals for Arc Reflection Cable Fault Location Based on Empirical Mode Decomposition[J]. Automation of Electric Power Systems, 2012, 36(11): 92-96.
Citation: LI Feng, XU Bing-yin, GONG Shi-ying. Extraction of Pulse Signals for Arc Reflection Cable Fault Location Based on Empirical Mode Decomposition[J]. Automation of Electric Power Systems, 2012, 36(11): 92-96.

基于EMD的电弧反射电缆故障测距脉冲信号提取方法

Extraction of Pulse Signals for Arc Reflection Cable Fault Location Based on Empirical Mode Decomposition

  • 摘要: 电弧反射法(ARM)电缆故障测距脉冲信号受故障电弧电压的干扰,故障反射脉冲不易识别,无法满足故障定位要求。分析了干扰产生的原因和受扰脉冲信号的特性,通过经验模态分解(EMD)分析了受扰脉冲信号的时频分布规律。论证了根据受扰脉冲信号特定时段均值特性可以对脉冲信号和干扰进行分离。据此提出基于受扰脉冲信号特定时段均值特性和EMD滤波的ARM脉冲信号提取算法。该方法不需要预定义信号分解层数和基函数,完全由数据驱动实现,因而具有较好的自适应能力。实测数据处理结果验证了算法的有效性。

     

    Abstract: Arc reflection method(ARM) is a kind of cable fault locating method widely used nowadays.Pulse echoes are subject to interference coming from the arc voltage fluctuation on fault spot when fault cable is tested using arc reflection method.For this reason,it is difficult to locate the fault spot by identifying the disordered features of pulse echoes.This paper analyzes the causes of arc voltage fluctuation when arc is burning as well as the features of the disturbed pulse signals,and then analyzes time-frequency distribution of the disturbed pulse signals using empirical mode decomposition(EMD).It proves that pulse signals and arc voltage fluctuation received in the same time can be completely separated according to their arithmetical mean characteristics in a specific period of time.A new extraction algorithm of pulse signals is proposed.It can be used for identifying arc reflection cable fault locations based on arithmetical mean characteristics of the distributed pulse signals over a specific period time.An empirical mode decomposition filter is also proposed.This method overcomes the difficulties in choosing signals basic functions and signals decomposition levels.It is fully data-driven,so it has strong adaptive capacity.Results of pulse signals extraction by recorded real test data proved the proposed algorithm.

     

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