基于EEMD样本熵的直流牵引网故障电流识别  

Fault current identification of DC traction network based on EEMD sample entropy

作　　者：冷月[1] 杨洪耕[1] 王智琦[1] 

机构地区：[1]四川大学电气信息学院,四川成都610065

出　　处：《中国测试》2016年第12期95-99,共5页China Measurement & Test

摘　　要：针对地铁直流牵引网的振荡电流容易引起继电保护系统频繁误动的问题,提出一种结合总体平均经验模态分解(ensemble empirical mode decomposition,EEMD)和样本熵的直流牵引网振荡电流与短路故障电流识别方法。利用EEMD方法对直流牵引网的馈线电流信号进行分解,求取各固有模态函数(intrinsic mode function,IMF)分量的样本熵值,并将计算结果求和,进而获得反映直流牵引网运行状态信息的特征量。通过对典型馈线电流信号进行分析计算,可知EEMD和样本熵相结合的特征提取方法可以有效地区分直流牵引网振荡电流与短路故障电流。算例分析验证该方法的有效性。Aiming at the frequent malfunctions of relay protection system caused by the oscillation current of Metro DC traction network, an identification approach of the oscillation current and the short-circuit fault current in DC traction network combined ensemble empirical mode decomposition （EEMD） with sample entropy is presented. In this method, the feeder current is decomposed by using EEMD method, and the sum of the sample entropy of each intrinsic mode function （IMF） is figured out. Thus, the feature which involves information on the operation state of DC traction network is acquired. From the analytical calculation of the typical feeder currents, it can be found out that the feature extraction method combined EEMD and sample entropy can effectively distinguish the oscillation current and the short-circuit fault current of DC traction network. Examples verify the effectiveness of the method.

关 键 词：短路故障电流 振荡电流 样本熵 总体平均经验模态分解 直流牵引网 

分 类 号：TM41[电气工程—电器]