基于小波包特征能量提取的变压器绕组变形故障诊断  

Transformer Winding Deformation Fault Diagnose Based on Energy Feature Extraction by Wavelet Packet

作　　者：钱苏翔[1,2] 杜琦[1,2] 顾小军[2] 李竹平[1,2] 

机构地区：[1]嘉兴学院机电工程学院,嘉兴314001 [2]常州大学机械工程学院,常州213016

出　　处：《机械设计与制造》2012年第9期135-137,共3页Machinery Design & Manufacture

基　　金：国家自然科学基金资助项目(50575095);浙江省教育厅重大科技攻关项目(ZD2009005)

摘　　要：变压器绕组发生不同类型和程度的故障时,在不同频带内的信号能量会发生改变,可以通过计算不同工况下绕组响应信号的各频带能量来诊断是否发生故障,故提出了一种基于频率响应法(Frequency Response Analysis,FRA)和小波包特征能量提取的变压器故障诊断方法,将待分析信号与小波基之间的最大互相关系数作为选择小波基的依据,对变压器绕组短路和径向移位进行研究,并进行了试验验证。试验结果表明,基于小波包的特征能量提取方法能有效地区分绕组故障类型和程度,提高了诊断的灵敏度。When different types of faults with varying degrees occur at transformer winding,the energy of the signals in different frequency bands will change.So it can calculate the energy of each band of different response signals under different circumstances to determine whether the winding failure.The trans-former fault diagnosis method based on FRA and characteristic energy extraction is presented,and the cross-correlation between the signal and the wavelet was taken as criterion to choose the wavelet.The short circuit and radial displacement fault of transformer winding are studied with above method.The method is verified by test.Experimental results show that this method can diagnose winding fault type and extent ef- fectively, and improve the sensitivity of fault diagnosis.

关 键 词：FRA 小波包 特征能量提取 绕组故障 诊断 

分 类 号：TH16[机械工程—机械制造及自动化] TM41[电气工程—电器]