LAPSE:2023.13249v1
Published Article

LAPSE:2023.13249v1
Research on Diagnosis and Prediction Method of Stator Interturn Short-Circuit Fault of Traction Motor
March 1, 2023
Abstract
The traction motor (TM) is an essential part of the high-speed train, the health condition of which determines the quality and safety of the vehicle. Hence, this study proposed a novel approach to diagnosing and predicting the TM stator interturn short-circuit fault (SISCF). Based on the Park vector (PV) of the stator current, this method could overcome the interference of current sensor errors, null shift, and motor frequency fluctuations in the actual conditions. More specifically, Park’s transformation was used to obtain the PV of the stator current. Then, the PV was fitted to obtain the elliptical trajectory and its parameters from which the negative sequence component of the stator current could be calculated. Finally, the SISCF diagnosis and prediction method were realized by the magnitude and trend of the negative current as well as the inclination of the trajectory ellipse. Furthermore, the performance of the proposed method was validated by a simulation model and a series of experiments. The simulation results were consistent with the experimental results, supporting the validity and correctness of the method proposed in this study.
The traction motor (TM) is an essential part of the high-speed train, the health condition of which determines the quality and safety of the vehicle. Hence, this study proposed a novel approach to diagnosing and predicting the TM stator interturn short-circuit fault (SISCF). Based on the Park vector (PV) of the stator current, this method could overcome the interference of current sensor errors, null shift, and motor frequency fluctuations in the actual conditions. More specifically, Park’s transformation was used to obtain the PV of the stator current. Then, the PV was fitted to obtain the elliptical trajectory and its parameters from which the negative sequence component of the stator current could be calculated. Finally, the SISCF diagnosis and prediction method were realized by the magnitude and trend of the negative current as well as the inclination of the trajectory ellipse. Furthermore, the performance of the proposed method was validated by a simulation model and a series of experiments. The simulation results were consistent with the experimental results, supporting the validity and correctness of the method proposed in this study.
Record ID
Keywords
fault diagnosis, fault prediction, Park vector, stator interturn short-circuit fault, track fitting, traction motors
Subject
Suggested Citation
Liu J, Tan H, Shi Y, Ai Y, Chen S, Zhang C. Research on Diagnosis and Prediction Method of Stator Interturn Short-Circuit Fault of Traction Motor. (2023). LAPSE:2023.13249v1
Author Affiliations
Liu J: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Tan H: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Shi Y: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Ai Y: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Chen S: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Zhang C: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Tan H: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Shi Y: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Ai Y: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Chen S: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Zhang C: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Journal Name
Energies
Volume
15
Issue
10
First Page
3759
Year
2022
Publication Date
2022-05-20
ISSN
1996-1073
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PII: en15103759, Publication Type: Journal Article
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LAPSE:2023.13249v1
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https://doi.org/10.3390/en15103759
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Mar 1, 2023
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