Online monitoring of ground-wall insulation condition in inverter-fed motors using multi-frequency characteristics of leakage current

被引：0
|
作者：
Zhang C. [1 ,2 ]
Niu F. [1 ,2 ,3 ]
Sun Q. [1 ,2 ]
Huang S. [4 ]
Zhang J. [5 ]
Li K. [1 ,2 ]
Fang Y. [3 ]
机构：
[1] Key Lab of Electromagnetic Field and Electrical Apparatus Reliability of Hebei Province, Hebei University of Technology, Tianjin
[2] State Key Lab of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin
[3] Changshu Switch Manufacturing Co., Ltd., Changshu
[4] College of Information Engineering, Beijing Institute of Petrochemical Technology, Beijing
[5] School of Electrical Engineering, Zhejiang University, Hangzhou
来源：
Dianji yu Kongzhi Xuebao/Electric Machines and Control | 2023年 / 27卷 / 08期
关键词：
condition assessment; frequency characteristics; ground-wall insulation; inverter-fed motor; leakage current; online monitoring;
D O I：
10.15938/j.emc.2023.08.007
中图分类号：
学科分类号：
摘要：
Stator ground-wall (GW) insulation failure will lead to severe short-circuit fault, greatly reducing the reliability of motor operation and even endangering personal safety. To ensure the safe and reliable operation of inverter-fed motor, it is necessary to monitor the GW insulation condition of inverter-fed motor in real time and provide fault warning. Firstly, through the GW insulation degradation equivalent circuit model of inverter-fed motor, the mathematical model of leakage current was established. Secondly, the frequency distribution of voltage and leakage current in inverter-fed motor was studied, and the influence of insulation impedance and degradation position on leakage current fundamental frequency multiplication, switching frequency multiplication and its sideband frequency was analyzed. Then, a monitoring method for GW insulation condition of inverter-fed motor based on multi-frequency characteristics of leakage current was proposed, which can not only assess the degree of insulation degradation, but also accurately identify insulation degradation position. Finally, this method was validated under various operating conditions of inverter-fed motor, and the experimental tests were consistent with the theoretical analysis, which verifies the effectiveness and accuracy of proposed online monitoring method for GW insulation. © 2023 Editorial Department of Electric Machines and Control. All rights reserved.
引用
收藏
页码：64 / 72
页数：8
相关论文
共 23 条
  • [1] STONE G C., Condition monitoring and diagnostics of motor and stator windings-a review, IEEE Transactions on Dielectrics & Electrical Insulation, 20, 6, (2013)
  • [2] (2016)
  • [3] ZHENG Dayong, ZHANG Pinjia, A review of fault diagnosis and online condition monitoring of stator insulation in AC electrical machine, Proceedings of the CSEE, 39, 2, (2019)
  • [4] STONE G C, SEDDING H G, CHAN C., Experience with online partial discharge measurement in high-voltage inverter-fed motors, IEEE Transactions on Industry Applications, 54, 1, (2018)
  • [5] HANBARI T, FARJAH A., A magnetic leakage flux-based approach for fault diagnosis in electrical machines, IEEE Sensors Journal, 14, 9, (2014)
  • [6] SUN Yuguang, YU Xiwen, WEI Kun, Et al., A new type of search coil for detecting inter-turn faults in synchronous machines, Proceedings of the CSEE, 34, 6, (2014)
  • [7] LI Hao, ZHAO Meng, XIANG Dawei, Et al., On-line monitoring of incipient turn insulation deterioration of servo motor using switching transient frequency response, Proceedings of the CSEE, 40, 10, (2020)
  • [8] XIANG Dawei, LI Hao, YAN Hao, Et al., Online monitoring of incipient turn insulation degradation for inverter-fed machine using sensitive tail component in pwm switching oscillations, IEEE Transactions on Power Electronics, 36, 8, (2021)
  • [9] LI Hao, GU Yi, XIANG Dawei, Et al., Online condition monitoring of line-end coils insulation for inverter-fed machine by switching oscillation mode decomposition, IEEE Transactions on Industrial Electronics, 69, 11, (2022)
  • [10] ZOELLER C, VOGELSBERGER M A, FASCHING R, Et al., Evaluation and current-response-based identification of insulation degradation for high utilized electrical machines in railway application, IEEE Transactions on Industry Applications, 53, 3, (2017)
123