基于小波变换与模极大值法的高速铁路牵引网故障行波波头自动识别算法
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Algorithm of High-speed Railway Traction Nets Fault Traveling Wave Head Automatic Recognition Based on Wavelet Transform and Modulus Maxima
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    摘要:

    为解决准确自动识别入射波和反射波的波头这一难题,提出了一种基于小波变换与模极大值法的行波波头自动识别方法。本文在分析小波变换及突变点检测原理的基础上,利用牵引变电所采集到的电压行波信号,进行多尺度一维离散小波变换,选用Daubechies小波族的db6为基本小波,并采用搜索模极大值的方法和约束函数判定,在模极大值点间进行自动识别起始波和反射波的波头位置。理论分析和现场数据验证结果表明,该算法切实有效,并能结合初始波与反射波间数据采样点数,得出故障初始行波脉冲与故障点反射回来的行波脉冲之间的时间差。

    Abstract:

    To solve the problem of automatic recognition wave head of the incident wave and reflection , Put forward a method of traveling wave head automatic identification based on the wavelet transform and modulus maxima. Based on the analysis of wavelet transform and the principle of mutation point detection, Using the voltage traveling wave signal collected in traction substation, more scales a dimensional discrete wavelet transform, Choosed db6 of the Daubechies wavelet family as basic wavelet, And used the searching modulus maxima method and the constraint function decision,to automatic recognition the wave head position of initial wave and reflection in the modulus maximum points. Theoretical analysis and the data validate the results indicate that the algorithm is practical and effective and combined with sampling points between the initial wave and reflection data,so as to obtained time difference between traveling wave pulseof the initial wave and wave reflected from fault point.

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吴仲朗. 基于小波变换与模极大值法的高速铁路牵引网故障行波波头自动识别算法[J]. 科学技术与工程, 2012, 12(22): .
wuzhonglang. Algorithm of High-speed Railway Traction Nets Fault Traveling Wave Head Automatic Recognition Based on Wavelet Transform and Modulus Maxima[J]. Science Technology and Engineering,2012,12(22).

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  • 收稿日期:2012-03-06
  • 最后修改日期:2012-03-13
  • 录用日期:2012-03-20
  • 在线发布日期: 2012-06-20
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