基于IMF能量矩和SVM的电力线路故障定位

针对配电网拓扑结构日益复杂化以及线路单相接地时故障信息难以提取等问题,提出了一种基于本征模函数IMF(Intrinsic Mode Function)特征能量矩的故障信息提取方法,并利用SVM进行故障定位。该方法首先利用经验模式分解(EMD)良好的局域化特征来量化故障信息,将故障电流信号分解得到多类IMF并在时域轴上对该IMF进行积分,从而得到能量矩特征故障向量,从能量矩中选取相关系数大的作为学习样本输入SVM分类器,得到故障线路分类模型,进而完成配电网的故障定位。基于66k V线路模型的仿真实验表明,该方法仅需测量故障电流,可以准确、有效地识别故障区段,可靠性高。

Fault Line Selection for Power Transmission Line Based on Intrinsic Mode Function Energy Moment and SVM

For the problem about complicated topological structures and hard-to-extract information when single-phase grounding fault occurs in power distribution network, in this paper a new method of fault selection based on the intrinsic-mode-function ( IMF ) energy moment and SVM ( Support Vector Machines ) is proposed for power system. The localization characteristics of EMD is used to quantify the fault, and then the SVM is combined to classify the fault. Firstly, the fault current signals are decomposed into certain IMF ( Intrinsic Mode Function ). Secondly, an integral of selected IMF components along time axis is calculated to obtain the IMF energy moment eigenvectors. Finally, the IMF energy moments of high correlation coefficient are taken as the eigenvectors to input into SVM classifier for fault selection. As a result, the fault selection model is obtained. A simulation results of 66kV line model show the proposed method can recognize fault line accurately and effectively with only measurement of fault current signals.