直流牵引网振荡电流与故障电流波形识别  

Waveform Identification of Oscillation Current and Fault Current in DC Traction Network

作　　者：田行军[1,2] 李夏青[1] 李运华[2] 

机构地区：[1]北京石油化工学院电气工程系,北京102617 [2]北京航空航天大学自动化科学与电气工程学院,北京100191

出　　处：《电工技术学报》2013年第11期247-253,共7页Transactions of China Electrotechnical Society

基　　金：北京市科技创新平台支持项目"牵引供电模拟实验;控制保护和测量系统"(0714-EMTC-2854/3/1)

摘　　要：地铁直流牵引网中经常出现的低频振荡电流导致继电保护系统误动频繁,欲提高保护系统的可靠性必须采取更为有效的特征提取方法。经验模态分解(EMD)和能量权重结合的特征提取方法可有效区分牵引网振荡电流与故障电流,实现对牵引网电流动态特征信息的提取。通过对牵引网电流进行EMD分解并计算各本征模态函数(IMF)的能量权重,构建基于能量权重的多尺度特征熵,进而获得故障模式识别的特征矢量。现场实测数据证明,该方法可准确识别牵引网振荡电流与故障电流,且概念清晰、算法简单。Due to the frequent malfunctions of relay protection system caused by low-frequency oscillation current of Metro DC traction network, more effective feature extraction methods are in need to improve the reliability of protection system. The extraction method combined empirical mode decomposition（EMD） and energy weight can effectively distinguish oscillation current and fault current of traction network, and achieve the extraction of dynamic feature information in traction network current. In this method, the traction network current is decomposed by EMD and the energy weight of each intrinsic mode function（IMF） is calculated, and the feature vector of fault pattern recognition is obtained by constructing the multi-scale feature entropy based on energy weight. The actual measurement data illustrates that the new method not only can accurately identify oscillation current and fault current of traction network, but also has the advantages of clear concept and simple algorithm.

关 键 词：直流牵引网 故障电流 振荡电流 EMD 特征熵 

分 类 号：TM922.3[电气工程—电力电子与电力传动] TM713