复杂工况下的永磁同步电机典型绕组故障在线诊断  

Online Diagnosis of Typical Winding Faults in Permanent Magnet Synchronous Motors under Complex Working Conditions

作　　者：刘蔚 李万铨 王明峤 郑萍[1] 赵志衡[1] Liu Wei;Li Wanquan;Wang Mingqiao;Zheng Ping;Zhao Zhiheng(School of Electrical Engineering and Automation Harbin Institute of Technology,Harbin 150001 China)

机构地区：[1]哈尔滨工业大学电气工程及自动化学院,哈尔滨150001

出　　处：《电工技术学报》2024年第6期1764-1776,共13页Transactions of China Electrotechnical Society

基　　金：国家自然科学基金资助项目(51991385)。

摘　　要：绕组故障作为永磁同步电机常见的故障之一,严重影响电机正常运行。但由于电机运行工况复杂,故障特征波动严重,基于故障特征的诊断精度较低。为提高复杂工况下绕组故障的诊断精度,该文提出一种复杂工况下基于控制器信号的在线诊断方法。首先,对典型绕组故障进行故障机理分析,并通过基于自适应随机窗的快速傅里叶变换(FFT),提取控制器信号的相应故障特征;其次,通过研究单一工况和复杂工况下的各故障特征分布,揭示部分故障特征会在低转速工况下失效;再次,定义了复杂工况下故障特征性能指标,用于筛选故障特征;最后,在人工神经网络的基础上,提出了深度优化人工神经网络,引入批量归一化(BN)算法,并对深度网络结构残差化,提高网络泛化能力和诊断准确性。实验结果表明,通过计算故障特征性能指标,能够在诊断前对故障特征进行有效筛选,且深度优化人工神经网络的诊断准确性高、泛化能力强,在复杂工况下能够实现电机典型绕组故障的精确在线诊断。Winding faults seriously affect the normal operation of permanent magnet synchronous motors.As one of the most widely used fault diagnosis methods,motor current signature analysis(MCSA)can effectively diagnose winding faults.However,its accuracy can be affected by complex operating conditions,sensor errors,and calculation errors.An online diagnosis method based on controller signals is proposed to ensure accurate fault diagnosis under complex working conditions without additional sensors.Firstly,winding faults are analyzed mechanically,and the transmission of fault information among the controller signals is revealed.When a winding failure occurs in the motor,the third harmonic appears in the phase current.After the Clarke and Park transform,the third harmonic component appears in the current in theαβaxis,making the second and fourth current harmonics appear in the dq axis.According to the dq-axis voltage-current equation and Park’s inverse transform,the dq-axis reference voltage has anomalous second and fourth harmonics,and theαβ-axis reference voltage has anomalous third harmonic.Secondly,the fault features are extracted through an adaptive random window based on a fast Fourier transform(FFT).An analysis of fault characteristic distribution under single and complex working conditions reveals that some fault characteristics may fail under low-speed working conditions.Based on the artificial neural network(ANN),a deep optimization ANN is proposed.By the batch normalization(BN)algorithm,the deep network structure is residual to improve network generalization ability and diagnostic accuracy.Finally,a performance index of fault characteristics under complex working conditions is defined.The experimental results show that when the motor operates at 500 r/min to 1250 r/min and 20 N·m to 40 N·m,the fault feature under single operating conditions approximately meets the normal distribution.The fault feature stability(Sf)under complex working conditions is defined based on the distribution variance.By the 3σprin

关 键 词：永磁同步电机 绕组故障 在线故障诊断 特征提取 深度优化人工神经网络 

分 类 号：TM307[电气工程—电机]