基于流形学习的PMSM早期匝间短路故障特征提取  

Feature extraction of inchoate interturn short circuit fault for PMSM based on manifold learning

作　　者：陈柄任 李颖晖[1] 李哲[1] 卢小勇[1] 刘聪[2] 

机构地区：[1]空军工程大学航空航天工程学院,陕西西安710038 [2]空军第一航空学院,河南信阳464000

出　　处：《电力系统保护与控制》2016年第17期18-24,共7页Power System Protection and Control

基　　金：国家973计划(2015CB755805)~~

摘　　要：针对永磁同步电机早期故障微弱特征难以提取的问题,借助Ansoft建立了永磁同步电机的二维瞬态有限元模型,仿真出短路1匝到7匝状态下电机的各项性能指标。通过小波包分析的方法对不同频带的能量特征进行分解,得到故障状态的高维特征。采用局部切空间排列法和其他几种流形学习方法对匝间短路早期故障进行降维,解得低维空间中的映射,并进行了实验验证。结果表明,流形学习方法可以有效地分类出故障与正常状态,且局部切空间排列法可以对短路匝数进行区分,为永磁同步电机故障的诊断和预测提供了一个新的思路。In most case, the incipient fault feature of interturn short circuit fault is difficult to extract, thus this paper provides a novel fault diagnosis method for the permanent magnet synchronous motor （PMSM） based on the local tangent space arrangement （LTSA）. Firstly, the two-dimension instantaneous finite element model of PMSM is established in Ansoft simulation, and the performance indexes are obtained accordingly. Afterwards, the performance indexes are decomposed to high dimensional fault features through the wavelet packet, and through LTSA and other manifold learning methods, it is reduced to gain the mapping in low dimensional space, which can classify faults and normal state. Finally, the experimental results show that the manifold learning method can effectively extract the incipient fault feature of interturn short circuit fault, additionally, LTSA can be used to distinguish the number of short circuit turns, which provide a new idea for fault diagnosis and prediction of PMSM. This work is supported by National Basic Research Program of China （No. 2015CB755805）.

关 键 词：永磁同步电动机 匝间短路 局部切空间排列法 ANSOFT 小波包分析 

分 类 号：TM341[电气工程—电机]