Article(id=1194958289231393074, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1194958282507923740, articleNumber=null, orderNo=null, doi=10.19457/j.1001-2095.dqcd25075, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1680796800000, receivedDateStr=2023-04-07, revisedDate=null, revisedDateStr=null, acceptedDate=null, acceptedDateStr=null, onlineDate=1762829857404, onlineDateStr=2025-11-11, pubDate=1732032000000, pubDateStr=2024-11-20, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1762829857404, onlineIssueDateStr=2025-11-11, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1762829857404, creator=13701087609, updateTime=1762829857404, updator=13701087609, issue=Issue{id=1194958282507923740, tenantId=1146029695717560320, journalId=1189987059142926344, year='2024', volume='54', issue='11', pageStart='3', pageEnd='96', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=1, articleOrder=1, issueType=-1, specialIssue=null, createTime=1762829855799, creator=13701087609, updateTime=1762830052947, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1194959109486265329, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1194958282507923740, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1194959109486265330, tenantId=1146029695717560320, journalId=1189987059142926344, issueId=1194958282507923740, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=76, endPage=80, ext={EN=ArticleExt(id=1194958289428525363, articleId=1194958289231393074, tenantId=1146029695717560320, journalId=1189987059142926344, language=EN, title=Diagnosis Method of Inter-turn Short Circuit Fault of Three-phase Induction Motor, columnId=null, journalTitle=Electric Drive, columnName=null, runingTitle=null, highlight=null, articleAbstract=

Three-phase asynchronous motor is one of the most frequently used motors. Inter-turn short circuit fault is a kind of fault with high probability. If the real-time monitoring of motor operation,health and safety evaluation of the operation,will be able to timely stop losses,reduce losses. An inter-turn fault diagnosis method was proposed based on extended Clarke transform and fault characteristic factor Q,analyzed the characteristics of Clarke vector of inter-turn fault of motor,modulated the Clarke vector of inter-turn fault of motor,and obtained the DC component of symmetrical component and the AC component twice the fundamental frequency in the three-phase current. The ratio of the amplitude of the component to the DC component was used as the fault characteristic factor Q to diagnose the inter-turn fault. Clarke vector spectrum was studied by characteristic factor Q,so as to judge the motor fault,and theoretical simulation and prototype test were carried out.

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三相异步电机是各类电机中应用量最大的一种,匝间短路故障为三相异步电机发生概率较大的一类故障。如果能实时监测电机运行状况,对运行进行健康安全评估,将能及时止损,减少损失。提出了基于扩展Clarke变换和故障特征因子Q的匝间故障诊断方法,分析了电机匝间故障的Clarke矢量的特点,对电机匝间故障的Clarke矢量取模,得到三相电流中对称分量的直流分量和2倍于基频的交流分量,以该分量的幅值与直流分量的比值作为故障特征因子Q 进行匝间故障诊断。通过特征因子Q研究Clarke矢量频谱,从而判断电机故障,并且进行了理论仿真以及样机测试。

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阚超豪(1974—),男,硕士,副教授,主要研究方向为新型电机故障检测,Email:

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阚超豪(1974—),男,硕士,副教授,主要研究方向为新型电机故障检测,Email:

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阚超豪(1974—),男,硕士,副教授,主要研究方向为新型电机故障检测,Email:

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The values of the fault characteristic factor Q of the prototype under different fault conditions and load conditions

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样机 空载 满载
正常运行 0.032 942 0.035 896
匝间短路1% 0.100 22 0.104 37
匝间短路2% 0.178 16 0.174 26
匝间短路3% 0.222 89 0.219 68
匝间短路5% 0.283 67 0.281 37
匝间短路10% 0.344 57 0.342 46
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样机在不同故障情况以及不同负载情况下的故障特征因子Q的值

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样机 空载 满载
正常运行 0.032 942 0.035 896
匝间短路1% 0.100 22 0.104 37
匝间短路2% 0.178 16 0.174 26
匝间短路3% 0.222 89 0.219 68
匝间短路5% 0.283 67 0.281 37
匝间短路10% 0.344 57 0.342 46
), ArticleFig(id=1194958715339125395, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1194958289231393074, language=EN, label=Tab.2, caption=

The values of the fault characteristic factor Q of the simulation model under different fault conditions and load conditions

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仿真模型 空载 满载
正常运行 0.004 649 0.004 893 6
匝间短路1% 0.068 821 0.070 989
匝间短路2% 0.137 14 0.134
匝间短路3% 0.184 76 0.180 03
匝间短路5% 0.240 82 0.239 49
匝间短路10% 0.302 21 0.300 25
), ArticleFig(id=1194958715448177302, tenantId=1146029695717560320, journalId=1189987059142926344, articleId=1194958289231393074, language=CN, label=表2, caption=

仿真模型在不同故障情况以及不同负载情况下的故障特征因子Q的值

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仿真模型 空载 满载
正常运行 0.004 649 0.004 893 6
匝间短路1% 0.068 821 0.070 989
匝间短路2% 0.137 14 0.134
匝间短路3% 0.184 76 0.180 03
匝间短路5% 0.240 82 0.239 49
匝间短路10% 0.302 21 0.300 25
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三相异步电机匝间短路故障的诊断方法
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阚超豪 , 姚骁键 , 齐安康 , 王志胜 , 王鹏程 , 陈功
电气传动 | 可靠性与诊断 2024,54(11): 76-80
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电气传动 | 可靠性与诊断 2024, 54(11): 76-80
三相异步电机匝间短路故障的诊断方法
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阚超豪 , 姚骁键, 齐安康, 王志胜, 王鹏程, 陈功
作者信息
  • 合肥工业大学 电气与自动化工程学院,安徽 合肥 230009
  • 阚超豪(1974—),男,硕士,副教授,主要研究方向为新型电机故障检测,Email:

Diagnosis Method of Inter-turn Short Circuit Fault of Three-phase Induction Motor
Chaohao KAN , Xiaojian YAO, Ankang QI, Zhisheng WANG, Pengcheng WANG, Gong CHEN
Affiliations
  • School of Electrical and Automation Engineering,Hefei University of Technology, Hefei 230009,Anhui,China
出版时间: 2024-11-20 doi: 10.19457/j.1001-2095.dqcd25075
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三相异步电机是各类电机中应用量最大的一种,匝间短路故障为三相异步电机发生概率较大的一类故障。如果能实时监测电机运行状况,对运行进行健康安全评估,将能及时止损,减少损失。提出了基于扩展Clarke变换和故障特征因子Q的匝间故障诊断方法,分析了电机匝间故障的Clarke矢量的特点,对电机匝间故障的Clarke矢量取模,得到三相电流中对称分量的直流分量和2倍于基频的交流分量,以该分量的幅值与直流分量的比值作为故障特征因子Q 进行匝间故障诊断。通过特征因子Q研究Clarke矢量频谱,从而判断电机故障,并且进行了理论仿真以及样机测试。

三相异步电机  /  匝间短路故障  /  Clarke矢量  /  特征因子Q

Three-phase asynchronous motor is one of the most frequently used motors. Inter-turn short circuit fault is a kind of fault with high probability. If the real-time monitoring of motor operation,health and safety evaluation of the operation,will be able to timely stop losses,reduce losses. An inter-turn fault diagnosis method was proposed based on extended Clarke transform and fault characteristic factor Q,analyzed the characteristics of Clarke vector of inter-turn fault of motor,modulated the Clarke vector of inter-turn fault of motor,and obtained the DC component of symmetrical component and the AC component twice the fundamental frequency in the three-phase current. The ratio of the amplitude of the component to the DC component was used as the fault characteristic factor Q to diagnose the inter-turn fault. Clarke vector spectrum was studied by characteristic factor Q,so as to judge the motor fault,and theoretical simulation and prototype test were carried out.

three-phase induction motor  /  inter-turn short circuit fault  /  Clarke vector  /  characteristic factor Q
阚超豪, 姚骁键, 齐安康, 王志胜, 王鹏程, 陈功. 三相异步电机匝间短路故障的诊断方法. 电气传动, 2024 , 54 (11) : 76 -80 . DOI: 10.19457/j.1001-2095.dqcd25075
Chaohao KAN, Xiaojian YAO, Ankang QI, Zhisheng WANG, Pengcheng WANG, Gong CHEN. Diagnosis Method of Inter-turn Short Circuit Fault of Three-phase Induction Motor[J]. Electric Drive, 2024 , 54 (11) : 76 -80 . DOI: 10.19457/j.1001-2095.dqcd25075
匝间短路故障为异步电机发生概率最大的一类故障[1-2],会给异步电机带来定子三相电流不对称、电机转矩减小、短路电流很大等危害,甚至烧毁电机[3]。对异步电机匝间短路故障进行及时准确的诊断,然后积极有效地采取相应解决措施,保障异步电机的正常运行,可以有效降低事故发生的概率,减少经济损失,缩减维修成本,保障人员和电力设备的安全[4-5]
线圈间的匝间绝缘破损导致线圈导体直接接触是异步电机发生匝间短路故障的根本原因[6-8],其具体可体现在这几个方面:1)电机制造工艺的不良。铜线的不平整、绝缘的缺陷、下线的碰撞以及整形的摩擦等都会导致线圈匝间绝缘的损伤,从而造成异步电机发生匝间短路故障;2)机械和电磁方面的影响。在异步电机运行的过程中,由于机械和电磁等综合应力的影响,电机的定子绕组将会产生振动、变形或者位移等现象,造成线圈匝间绝缘的磨损等,导致异步电机匝间短路故障的发生;3)工作条件恶劣,如异步电机在潮湿的环境下工作,会导致匝间绝缘电阻的电阻值下降;4)过电压等偶然因素的影响[9-10]。过电压有时会使线圈间的匝间绝缘被击穿,造成线圈间的匝间绝缘产生破损,从而导致异步电机发生匝间短路故障。
目前,关于异步电机匝间短路故障的诊断方法有很多,大致可以分为基于解析模型的诊断方法、基于信号处理的诊断方法以及基于知识的诊断方法这三类。基于解析模型的诊断方法,通过对异步电机的结构、绝缘和电磁情况等进行分析,建立异步电机在匝间短路故障状态下的解析模型,并在此解析模型的基础上研究异步电机发生匝间短路故障时的特征[11],该诊断方法已经取得了非常丰富的理论成果。由于难以对被诊断的异步电机建立精准的解析模型,所以运用该诊断方法所诊断出的结果值得怀疑,其成功应用的例子也比较少。基于信号处理的诊断方法,直接运用各种信号处理方法,例如傅里叶变换、阶次跟踪、倒频谱、希尔伯特变换和小波变换等,具有较强的适用性[12]。不仅如此,归为这一类的诊断方法也是很多,比如定子电流频谱分析法、局部放电法、电机漏磁感应法、对称分量法、派克矢量法、振动信号分析法以及小波变换法等。基于知识的诊断方法,充分利用现有的电机故障诊断知识,引入异步电机多方面的历史数据,运用神经网络、模糊逻辑、模式识别、专家系统以及遗传算法等人工智能技术,进行异步电机匝间短路故障的诊断[13]。该诊断方法具有强大的诊断能力,是未来异步电机匝间短路故障诊断的主流方法,目前国内外对这一诊断方法都进行了研究。然而该诊断方法依赖于大量的电机历史数据,在通用性和适应性方面还有待提高。相信随着人工智能技术的不断发展,这一诊断方法会有更大的突破与创新。
为了及时可靠地诊断出异步电机匝间短路故障的发生,本文主要研究三相异步电机的匝间短路故障,其研究的目的在于能够找到一种更加准确、更加可行的三相异步电机匝间短路故障的诊断方法。因此本文提出基于扩展Clarke变换[14-15]利用故障特征因子Q的匝间故障诊断方法,分析了电机匝间故障的Clarke矢量的特点,对电机匝间故障的Clarke矢量取模,得到三相电流中对称分量的直流分量和2倍于基频的交流分量,以该分量的幅值与直流分量的比值作为故障特征因子进行匝间故障诊断,快速准确地判断出异步电机匝间短路故障的严重程度及故障相。样机试验验证了本文所提方法的正确性。
Clarke变换[16],即通过坐标变换将定子三相电流 i A i B i C从三相平面静止坐标系OABC下变换到两相平面静止坐标系Oαβ变换下。图1为Clarke坐标变换图,展示了三相平面静止坐标系OABC下的电流 i A i B i C所产生的磁动势与两相平面静止坐标系Oαβ下产生的电流 i α i β所产生的磁动势之间的位置关系,且选定A轴与α轴相重合。
Clarke矢量矩阵式如下式:
i α i β = 2 3 1 1 / 2 1 / 2 0 3 / 2 3 / 2 i A i B i C
Clarke矢量为 i α + j i β,其轨迹和模的频谱都可以用来诊断异步电机匝间短路故障。Clarke矢量的轨迹,可以用其发生匝间短路故障时形状由一个正圆畸变为一个椭圆的特点来诊断故障的发生,用椭圆率会随匝间短路故障严重程度的增大而增大的特点来诊断故障的严重程度。长轴方向指向故障相,频谱模即故障特征因子Q表示严重程度。首先检测异步电机的定子三相电流,通过Clarke变换将定子三相电流 i A i B i C变换成Clarke矢量的两个分量 i α i β,然后以 i α为横坐标、 i β为纵坐标作出Clarke矢量的轨迹,再对Clarke矢量模的平方 i α 2 + i β 2进行频谱分析得到Clarke矢量模的频谱,就可通过Clarke矢量的轨迹和Clarke矢量模的频谱诊断出异步电机匝间短路故障的发生、严重程度以及故障相。
仿真步长设置为0.001 s,仿真时长设置为2 s,分别运行在不同故障情况以及不同负载情况下的仿真模型,仿真所检测到的定子三相电流 i A i B i C即为所要的仿真运行结果,如图2~图4所示。
图2可观察到,仿真模型在正常运行的情况下,定子三相电流 i A i B i C对称;仿真模型在匝间短路的故障情况下,定子三相电流 i A i B i C不对称。除此之外,由图3图4还可观察到,不管仿真模型是在正常运行的情况下还是在匝间短路的故障情况下,负载情况的变化只会引起定子三相电流幅值的变化,而不会引起定子三相电流相位的变化,这种幅值的变化指的是定子三相电流 i A i B i C的幅值等比例地变化。由图3图4可观察到,仿真模型在匝间短路越严重的故障情况下,定子三相电流的不对称程度越高。此外,还可观察到,仿真模型匝间短路的故障相不同,定子三相电流 i A i B i C的幅值大小关系将有所不同,故障相的电流幅值总比非故障相的电流幅值要大。
用Matlab这一数据处理软件对在不同故障情况以及不同负载情况下的仿真运行结果分别进行数据处理,得到对应情况下的仿真处理结果,即Clarke矢量的轨迹和Clarke矢量模的频谱。
在计算机仿真方面,主要运用Ansoft16这一有限元电机设计软件,对一台额定功率为3 kW的三相异步电机进行关于匝间短路故障的模型搭建,且同时模拟各种故障情况,进行仿真运行,最后数据处理。图5~图10为各种故障条件下的矢量轨迹和频谱。由其它实验可以得出Clarke矢量的轨迹和Clarke矢量模的频谱都不受负载情况的影响。因此该方法适用于任何负载情况下的检测。同时在计算机仿真方面,基于Clarke矢量的匝间短路故障诊断方法在诊断故障发生、诊断故障严重程度、诊断故障相以及考虑负载情况的影响这四个方面均具有可行性。
图11为样机实测验证平台。采用的样机的型号是YZR112M-4。将电机连接之后,主要进行不同情况下各种故障测试。用示波器检测样机在不同故障或负载情况下的定子三相电流,如图12所示。
然后运用Matlab这一计算机软件对测得的定子三相电流 i A i B i C进行数据处理,即可得到对应情况下的Clarke矢量的轨迹和Clarke矢量模的频谱。最后根据Clarke矢量的频谱以及模值,进行电机健康评估,并且进行验证。同时对理论仿真的各种情况都进行了实测验证,由于篇幅有限不一一列举。表1表2是在不同负载状况下进行理论和实际Q值的对比结果。
表1表2分析可得:1)样机在正常运行的情况下,故障特征因子Q的值趋近于0。2)样机在匝间短路的故障情况下,故障特征因子Q的值不为0,且远远大于0。样机在匝间短路越严重的故障情况下,故障特征因子Q的值越大。3)不管样机是在正常运行的情况下还是在匝间短路的故障情况下,故障特征因子Q在不同负载情况下的值都近似相等。
最后根据理论和实际对比的数据可以肯定理论仿真的可行性即基于Clarke矢量的匝间短路故障诊断方法在诊断故障发生断故障严重程度、诊断故障相以及考虑负载情况的影响这4个方面均具有可行性。
本文提出了基于扩展Clarke变换和故障特征因子Q的匝间故障诊断方法,并对该方法进行了理论分析、样机仿真和试验,结论如下:1)在不同短路情况下,故障特征因子Q的值随着短路严重程度而增大。2)故障特征因子Q的值不随着负载状况不同而改变。综上,样机试验验证了本文所提方法的可行性,在实现电机健康状态评估方面具有良好的应用前景。
  • 大学生创新创业训练计划项目基金(S202210359072)
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doi: 10.19457/j.1001-2095.dqcd25075
  • 接收时间:2023-04-07
  • 首发时间:2025-11-11
  • 出版时间:2024-11-20
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  • 收稿日期:2023-04-07
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大学生创新创业训练计划项目基金(S202210359072)
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    合肥工业大学 电气与自动化工程学院,安徽 合肥 230009
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