基于随机森林算法的V2G充电桩故障诊断研究  

Research on fault diagnosis of V2G charging pile based on random forest algorithm

作　　者：王群飞 尹忠东 鄂涛 WANG Qunfei;YIN Zhongdong;E Tao(State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electrical Power University),Beijing 102206,China)

机构地区：[1]新能源电力系统国家重点实验室(华北电力大学),北京102206

出　　处：《电测与仪表》2024年第8期111-118,共8页Electrical Measurement & Instrumentation

基　　金：国家重点研发计划项目(2016YFB0101903)。

摘　　要：针对V2G直流充电桩开关模块的故障诊断问题,提出一种基于随机森林算法的诊断方法。该方法使用小波包分析提取故障电流信号中的特征信息,使用核主成分分析降维以降低样本数据的复杂程度,使用随机森林算法训练出适用于直流充电桩开关模块的故障诊断器。实验验证表明,采用随机森林故障诊断器可以有效诊断开关模块故障。该方案在直流充电桩开关模块故障诊断方面具有现实意义。In order to solve the problem of fault diagnosis of switch module of V2G DC charging pile,a diagnosis method based on random forest algorithm is proposed in this paper.The method adopts wavelet packet analysis to extract the feature information of fault current signal,and uses kernel principal component analysis to reduce the complexity of sample data.The fault diagnosis device suitable for DC charging pile switch module is trained by using random forest algorithm.The experimental results show that the random forest fault diagnosis can effectively diagnose the switch module fault.The scheme is of practical significance in the fault diagnosis of DC charging pile switch module.

关 键 词：充电设备 开关模块 故障诊断 随机森林算法 小波包分解 核主成分分析 

分 类 号：TM910.6[电气工程—电力电子与电力传动]