Enhanced robust diagnosis of multiple-type demagnetisation fault for permanent magnet synchronous motor based on D-axis magnetic network model using magnetic field reconstruction

IF 1.5 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC Iet Electric Power Applications Pub Date : 2023-11-20 DOI:10.1049/elp2.12391
Wang He, Jun Hang, Shichuan Ding
{"title":"Enhanced robust diagnosis of multiple-type demagnetisation fault for permanent magnet synchronous motor based on D-axis magnetic network model using magnetic field reconstruction","authors":"Wang He,&nbsp;Jun Hang,&nbsp;Shichuan Ding","doi":"10.1049/elp2.12391","DOIUrl":null,"url":null,"abstract":"<p>Demagnetisation fault (DF) is a common rotor fault in the permanent magnet synchronous motor (PMSM). DF can cause obvious changes in the magnetic field of PMSMs. As a result, previous DF diagnosis methods mainly depends on magnetic field analysis. However, conventional analysis methods based on the magnetic potential-permeability method, sub-domain method and magnetic equivalent circuit method are not suitable for DF condition. In addition, DF diagnosis is further complicated by various operating conditions. To address these issues, a robust DF diagnosis method is proposed for PMSM based on <i>d</i>-axis magnetic network model using magnetic field reconstruction. The proposed fault diagnosis method employs magnetic field reconstruction to obtain the radial air-gap flux density under the open-circuit condition. Subsequently, a <i>d</i>-axis magnetic network is established to solve the demagnetisation coefficient matrix in the DF state. Finally, the effectiveness of the proposed method is validated through simulations and experiments. Both results demonstrate that the proposed method can accurately recognise uniform DF and partial DF with multiple demagnetised permanent magnets, exhibiting great robustness against different operating conditions.</p>","PeriodicalId":13352,"journal":{"name":"Iet Electric Power Applications","volume":"18 3","pages":"325-335"},"PeriodicalIF":1.5000,"publicationDate":"2023-11-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1049/elp2.12391","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Iet Electric Power Applications","FirstCategoryId":"5","ListUrlMain":"https://ietresearch.onlinelibrary.wiley.com/doi/10.1049/elp2.12391","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

Abstract

Demagnetisation fault (DF) is a common rotor fault in the permanent magnet synchronous motor (PMSM). DF can cause obvious changes in the magnetic field of PMSMs. As a result, previous DF diagnosis methods mainly depends on magnetic field analysis. However, conventional analysis methods based on the magnetic potential-permeability method, sub-domain method and magnetic equivalent circuit method are not suitable for DF condition. In addition, DF diagnosis is further complicated by various operating conditions. To address these issues, a robust DF diagnosis method is proposed for PMSM based on d-axis magnetic network model using magnetic field reconstruction. The proposed fault diagnosis method employs magnetic field reconstruction to obtain the radial air-gap flux density under the open-circuit condition. Subsequently, a d-axis magnetic network is established to solve the demagnetisation coefficient matrix in the DF state. Finally, the effectiveness of the proposed method is validated through simulations and experiments. Both results demonstrate that the proposed method can accurately recognise uniform DF and partial DF with multiple demagnetised permanent magnets, exhibiting great robustness against different operating conditions.

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于 D 轴磁网络模型的永磁同步电机多类型退磁故障鲁棒诊断(使用磁场重构技术
退磁故障(DF)是永磁同步电机(PMSM)中常见的转子故障。DF 会导致 PMSM 的磁场发生明显变化。因此,以往的 DF 诊断方法主要依赖于磁场分析。然而,基于磁势渗透法、子域法和磁等效电路法的传统分析方法并不适用于 DF 状态。此外,各种运行条件也使 DF 诊断变得更加复杂。为解决这些问题,我们提出了一种基于 d 轴磁网络模型的 PMSM 故障诊断方法。所提出的故障诊断方法利用磁场重构来获取开路条件下的径向气隙磁通密度。随后,建立一个 d 轴磁网络,以求解 DF 状态下的退磁系数矩阵。最后,通过模拟和实验验证了所提方法的有效性。这两项结果表明,所提出的方法可以准确识别多块退磁永磁体的均匀 DF 和部分 DF,并在不同的运行条件下表现出很强的鲁棒性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Iet Electric Power Applications
Iet Electric Power Applications 工程技术-工程:电子与电气
CiteScore
4.80
自引率
5.90%
发文量
104
审稿时长
3 months
期刊介绍: IET Electric Power Applications publishes papers of a high technical standard with a suitable balance of practice and theory. The scope covers a wide range of applications and apparatus in the power field. In addition to papers focussing on the design and development of electrical equipment, papers relying on analysis are also sought, provided that the arguments are conveyed succinctly and the conclusions are clear. The scope of the journal includes the following: The design and analysis of motors and generators of all sizes Rotating electrical machines Linear machines Actuators Power transformers Railway traction machines and drives Variable speed drives Machines and drives for electrically powered vehicles Industrial and non-industrial applications and processes Current Special Issue. Call for papers: Progress in Electric Machines, Power Converters and their Control for Wave Energy Generation - https://digital-library.theiet.org/files/IET_EPA_CFP_PEMPCCWEG.pdf
期刊最新文献
Study of the Overvoltage and Its Distribution Characteristics in an Oil-Immersed Iron-Core Reactor Disconnected by an SF6 Circuit Breaker Speed-Sensorless Model-Free Predictive Torque Control for Induction Motor Drive Research on Peak-to-Average Power Ratio Control Method for Switched Reluctance Pulse Generator Harmonic Transient Modelling of Three-Phase Induction Motors Considering Non-Sinusoidal Power Supply Magnetic Field Analysis of Multi-Segment Modulated Pole Motors Based on the Air Gap Domain Multi-Harmonic Method
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1