基于EMD和Teager能量算子的电缆局部放电辨识

针对矿区电缆的局部放电辨识问题，根据其传播特性，提出利用经验模式分解与Teager能量算子对电缆两端测量点处局部放电信号的初始波头进行辨识，提升波头辨识方法的抗噪声干扰能力。采用RBF神经网络对训练样本进行训练，结合局部放电信号到达电缆两端测量点的时间差，实现电缆局部放电的精确辨识。利用PSCAD/EMTDC搭建电缆的仿真电路，并结合MATLAB进行了大量的仿真试验与计算分析。仿真计算结果表明该方法辨识精度高、相对误差小。

Cable partial discharge identification based on EMD and Teager energy operator

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.