基于EMD和Teager能量算子的电缆局部放电辨识
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TM85

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国家自然科学基金资助项目(51504253);国家电网有限公司科技项目“基于暂态扰动的配网电缆早期故障预警和定位方法研究”(J2018078)


Cable partial discharge identification based on EMD and Teager energy operator
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    摘要:

    交联聚乙烯(XLPE)电缆在矿区电网中普遍使用,因运行环境恶劣,电缆局部放电(PD)现象时有发生。首先,针对矿区电缆的PD辨识问题,对PD信号在XLPE电缆中的传播特性进行研究。其次,根据其传播特性,提出利用经验模态分解(EMD)与Teager能量算子相结合的方法,对电缆两端测量点处PD信号的初始波头进行辨识,该方法大大提升了波头辨识的抗噪声干扰能力。然后,采用径向基(RBF)神经网络对训练样本进行训练,结合PD信号到达电缆两端测量点的时间差,实现XLPE电缆PD精确定位。最后,通过搭建PSCAD/EMTDC电缆仿真电路验证了所提方法PD辨识精度高、辨识误差小的结论。

    Abstract:

    Cross linked polyethylene(XLPE) cables are commolly used in the power grid of mining area, but the operation environment is relatively bad and partial discharge(PD) of cables often occurs.Aiming at the problem of PD identification in the cable, the propagation characteristics of PD signal in XLPE cable are studied.According to propagation characteristics, method combining empirical mode decomposition(EMD) with Teager energy operator is proposed to identify the initial wave head of PD signal at both ends of the cable.The method greatly improves the ability of anti-noise in wave head identification.The radial basis function(RBF) neural network is used to train the training samples.Combined with the time difference between the PD signal and the measurement point at both ends of the cable, the accurate location of PD in XLPE cable is realized.PSCAD/EMTDC is used to build the cable simulation circuit.Simulation results show that proposed method has high PD identification accuracy and small identification error.

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刘波,孟祥震,迟鹏,聂鹏飞,丁然,梁睿.基于EMD和Teager能量算子的电缆局部放电辨识[J].电力工程技术,2020,39(5):36-42

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  • 收稿日期:2020-03-03
  • 最后修改日期:2020-04-15
  • 录用日期:2020-03-11
  • 在线发布日期: 2020-09-30
  • 出版日期: 2020-09-28
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