Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM
In this article, an inter-turn short-circuit (ITSC) fault diagnosis and severity estimation method based on extended state observer (ESO) and convolutional neural network (CNN) is proposed for five-phase permanent magnet synchronous motor (PMSM) drives. The relationship between fault parameters and...
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| Format: | Article |
| Language: | English |
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China Electrotechnical Society
2025-06-01
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| Series: | CES Transactions on Electrical Machines and Systems |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11066212 |
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| author | Yijia Huang Wentao Huang Tinglong Pan Dezhi Xu |
| author_facet | Yijia Huang Wentao Huang Tinglong Pan Dezhi Xu |
| author_sort | Yijia Huang |
| collection | DOAJ |
| description | In this article, an inter-turn short-circuit (ITSC) fault diagnosis and severity estimation method based on extended state observer (ESO) and convolutional neural network (CNN) is proposed for five-phase permanent magnet synchronous motor (PMSM) drives. The relationship between fault parameters and motor parameters is analyzed and the equivalent model of ITSC faults in the natural reference frame is accordingly derived. To achieve fault detection and location, the short-circuit turn ratio and short-circuit current are integrated as the fault diagnosis index. According to the model of the shortcircuit current, an ESO is designed for the estimation of the fault diagnosis index. Further, the sensitivity analysis among fault parameters is conducted to evaluate the short-circuit turn ratio and the short-circuit resistance. Subsequently, the postfault current, back electromotive force, electrical angular velocity, q1-axis current reference and the fault diagnosis index are selected as the input signals of CNN to estimate the short-circuit turn ratio. This approach not only resolves parameter coupling challenges but also provides a quantitative assessment of fault severity. Finally, simulations and experiments under different operating points validate the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-cfb5bee541e1439d82de0164d63fb78d |
| institution | Matheson Library |
| issn | 2096-3564 2837-0325 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | China Electrotechnical Society |
| record_format | Article |
| series | CES Transactions on Electrical Machines and Systems |
| spelling | doaj-art-cfb5bee541e1439d82de0164d63fb78d2025-07-04T06:28:17ZengChina Electrotechnical SocietyCES Transactions on Electrical Machines and Systems2096-35642837-03252025-06-019222423310.30941/CESTEMS.2025.00019Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSMYijia Huang0Wentao Huang1Tinglong Pan2Dezhi Xu3 School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China School of Internet of Things Engineering, Jiangnan University, Wuxi 214122, ChinaSchool of Internet of Things Engineering, Jiangnan University, Wuxi 214122, China School of Electrical Engineering, Southeast University, Nanjing 210096, ChinaIn this article, an inter-turn short-circuit (ITSC) fault diagnosis and severity estimation method based on extended state observer (ESO) and convolutional neural network (CNN) is proposed for five-phase permanent magnet synchronous motor (PMSM) drives. The relationship between fault parameters and motor parameters is analyzed and the equivalent model of ITSC faults in the natural reference frame is accordingly derived. To achieve fault detection and location, the short-circuit turn ratio and short-circuit current are integrated as the fault diagnosis index. According to the model of the shortcircuit current, an ESO is designed for the estimation of the fault diagnosis index. Further, the sensitivity analysis among fault parameters is conducted to evaluate the short-circuit turn ratio and the short-circuit resistance. Subsequently, the postfault current, back electromotive force, electrical angular velocity, q1-axis current reference and the fault diagnosis index are selected as the input signals of CNN to estimate the short-circuit turn ratio. This approach not only resolves parameter coupling challenges but also provides a quantitative assessment of fault severity. Finally, simulations and experiments under different operating points validate the effectiveness of the proposed method.https://ieeexplore.ieee.org/document/11066212multi-phase drivepermanent magnet synchronous motorinter-turn short-circuitfault diagnosis |
| spellingShingle | Yijia Huang Wentao Huang Tinglong Pan Dezhi Xu Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM CES Transactions on Electrical Machines and Systems multi-phase drive permanent magnet synchronous motor inter-turn short-circuit fault diagnosis |
| title | Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM |
| title_full | Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM |
| title_fullStr | Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM |
| title_full_unstemmed | Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM |
| title_short | Inter-turn Short-circuit Fault Diagnosis and Severity Estimation for Five-phase PMSM |
| title_sort | inter turn short circuit fault diagnosis and severity estimation for five phase pmsm |
| topic | multi-phase drive permanent magnet synchronous motor inter-turn short-circuit fault diagnosis |
| url | https://ieeexplore.ieee.org/document/11066212 |
| work_keys_str_mv | AT yijiahuang interturnshortcircuitfaultdiagnosisandseverityestimationforfivephasepmsm AT wentaohuang interturnshortcircuitfaultdiagnosisandseverityestimationforfivephasepmsm AT tinglongpan interturnshortcircuitfaultdiagnosisandseverityestimationforfivephasepmsm AT dezhixu interturnshortcircuitfaultdiagnosisandseverityestimationforfivephasepmsm |