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井下电力电缆故障定位研究

商立群 张少强 荣相 刘江山 王越

商立群,张少强,荣相,等. 井下电力电缆故障定位研究[J]. 工矿自动化,2024,50(2):130-137.  doi: 10.13272/j.issn.1671-251x.2023080014
引用本文: 商立群,张少强,荣相,等. 井下电力电缆故障定位研究[J]. 工矿自动化,2024,50(2):130-137.  doi: 10.13272/j.issn.1671-251x.2023080014
SHANG Liqun, ZHANG Shaoqiang, RONG Xiang, et al. Research on fault positioning of underground power cable[J]. Journal of Mine Automation,2024,50(2):130-137.  doi: 10.13272/j.issn.1671-251x.2023080014
Citation: SHANG Liqun, ZHANG Shaoqiang, RONG Xiang, et al. Research on fault positioning of underground power cable[J]. Journal of Mine Automation,2024,50(2):130-137.  doi: 10.13272/j.issn.1671-251x.2023080014

井下电力电缆故障定位研究

doi: 10.13272/j.issn.1671-251x.2023080014
基金项目: 陕西省自然科学基础研究计划资助项目(2021JM-393);天地科技股份有限公司科技创新创业资金专项 ( 2023-TD-ZD001-006 )。
详细信息
    作者简介:

    商立群(1968—),男,河南济源人,教授,博士,主要研究方向为电力系统故障定位,E-mail:shanglq@ xust.edu.cn

    通讯作者:

    张少强(1995—),男, 河北邯郸人,硕士研究生,主要研究方向为井下电力电缆故障定位,E-mail: 895370713 @qq.com。

  • 中图分类号: TD714

Research on fault positioning of underground power cable

  • 摘要: 针对传统井下电力电缆故障定位方法依赖主观参数选择和抗噪性能较差,无法满足强噪声背景下井下电力电缆故障精确定位要求的问题,提出了一种基于樽海鞘群算法(SSA)优化变分模态分解(VMD)并结合改进型Teager能量算子(NTEO)的井下电力电缆故障定位方法。针对VMD在信号分解上存在的模态混叠、过分解和欠分解问题,采用SSA以模糊熵为适应度函数对VMD模态数K和惩罚因子$ \alpha $ 2个参数进行优化,得到更能反映故障特征信息的本征模态函数;采用NTEO对本征模态函数进行首波波头标定,得到首末两端的波头到达时刻,根据双端测距法得出故障位置。采用PSCAD/EMTDC进行井下电力电缆故障仿真,模拟具有强背景噪声的井下故障信号,结果表明:① 在理想电流信号中加入9 ,12 dB噪声后,SSA−VMD的信噪比最低,皮尔逊相关系数最大,说明SSA−VMD在最大程度降噪的同时,能很好地保留信号的特征信息。② 在不同过渡电阻下,SSA−VMD−NTEO的定位精度较高。③ 在不同故障相角下,SSA−VMD−NTEO在采样点上出现不同,但定位位置没有改变,依旧保持较高的定位精度。④ 在不同故障距离下,SSA−VMD−NTEO均能保证较高的定位精度。⑤ 在井下较大噪声和10 MHz采样频率下,SSA−VMD−NTEO较小波模极大值和VMD+NTEO 2种方法的定位精度具有明显优势。

     

  • 图  1  测试信号时域与频域波形

    Figure  1.  Testing signal in time-domain and frequency-domain

    图  2  VMD分解下信号中心频率分布

    Figure  2.  Signal center frequency distribution under VMD decomposition

    图  3  井下电力电缆工作模型

    Figure  3.  Underground power cable working model

    图  4  故障前后A相电流波形

    Figure  4.  Current waveform of phase A before and after fault

    图  5  M侧各参数FE对比

    Figure  5.  Comparison of fuzzy entropy (FE) of various parameters on M-side

    图  6  N侧各参数FE对比

    Figure  6.  Comparison of fuzzy entropy (FE) of various parameters on N-side

    图  7  NTEO双端瞬时能量谱

    Figure  7.  NTEO dual terminal instantaneous energy spectrum

    图  8  TEO双端瞬时能量谱

    Figure  8.  TEO dual terminal instantaneous energy spectrum

    图  9  测试函数时域波形

    Figure  9.  Testing function in time-domain

    图  10  各算法所得IMF分量的FE

    Figure  10.  Fuzzy entropy (FE) of IMF obtained by each algorithm

    表  1  不同算法的信号分解结果

    Table  1.   Filtering results of different algorithms

    算法 SNR/dB PCC
    9 dB噪声 12 dB噪声 9 dB噪声 12 dB噪声
    小波硬阈值 9.82 12.77 0.973 0.965
    小波软阈值 9.43 12.76 0.986 0.976
    VMD 9.33 12.76 0.990 0.989
    SSA−VMD 9.15 12.19 0.996 0.998
    下载: 导出CSV

    表  2  不同过渡电阻下故障定位结果

    Table  2.   Fault positioning results under different transition resistance

    过渡电阻/Ω 波头采样点 定位位置/m 定位误差/m
    M侧 N侧
    0.1 5 032 5 022 599.13 0.87
    10 5 032 5 022 599.13 0.87
    1 000 5 032 5 022 599.13 0.87
    下载: 导出CSV

    表  3  不同故障相角下故障定位结果

    Table  3.   Fault positioning results under different fault phase angles

    故障相角/(°) 波头采样点 定位位置/m 定位误差/m
    M侧 N侧
    0 5 033 5 023 599.13 0.87
    30 5 032 5 022 599.13 0.87
    60 5 029 5 019 599.13 0.87
    90 5 030 5 020 599.13 0.87
    下载: 导出CSV

    表  4  不同故障距离下故障定位结果

    Table  4.   Fault positioning results under different fault distances

    故障距离/m 波头采样点 定位位置/m 定位误差/m
    M侧 N侧
    100 5 011 5 051 103.57 3.57
    200 5 098 5 128 202.61 2.61
    300 5 042 5 062 301.74 1.74
    400 5 061 5 071 400.87 0.87
    500 5 059 5 059 500.00 0
    600 5 032 5 022 599.13 0.87
    700 5 067 5 047 698.26 1.74
    800 5 143 5 113 797.39 2.61
    900 5 125 5 185 896.43 3.57
    下载: 导出CSV

    表  5  不同方法的故障定位结果

    Table  5.   Fault positioning results of different methods

    故障位置/m方法定位位置/m定位误差/m
    400小波模极大值419.9619.96
    VMD+NTEO409.049.04
    SSA−VMD−NTEO400.870.87
    800小波模极大值837.0437.04
    VMD+NTEO817.2117.21
    SSA−VMD−NTEO797.392.61
    下载: 导出CSV
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  • 收稿日期:  2023-08-04
  • 修回日期:  2024-01-28
  • 网络出版日期:  2024-03-01

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