A Novel Method for Monitoring Stator Interturn Insulation Degradation of Inverter-Fed Motors Based on Impedance Spectrum Division
The switching transient response method has become popular in recent years for monitoring of stator insulation in inverter-fed motors (IFMs). It can detect early insulation degradation and thus prevent serious insulation failures. However, for interturn insulation monitoring, the relationship between the transient response in phase current and interturn insulation degradation is not clear. In this article, a novel method for monitoring stator interturn insulation in IFMs based on switching transient current response is proposed. A lumped-parameter model of stator winding is established, and the division of winding impedance spectrum for insulation condition monitoring based on this model is analyzed. Then, the effect of interturn insulation degradation on the different regions of winding impedance spectrum is verified, and an interturn insulation monitoring indicator (IIMI) extracted from the transient current response based on the impedance spectrum division is proposed. The determination of qualified PWM excitation is also analyzed. Finally, the proposed method was verified using a 3 kW PMSM under field oriented control, and the experimental results show that the proposed IIMI is highly sensitive to the interturn insulation degradation.
Authors
- Feng Zhou (ORCID: https://orcid.org/0000-0002-5513-743X)
- Feng Niu (ORCID: https://orcid.org/0000-0003-2465-647X)
- Pingli Zhang (ORCID: https://orcid.org/0009-0008-3551-1683)
- Anmol Aggarwal (ORCID: https://orcid.org/0000-0001-8477-7324)
- Lu Zhang (ORCID: https://orcid.org/0000-0002-7145-7724)
- Shaopo Huang (ORCID: https://orcid.org/0000-0002-8662-9496)
- Zijun Xu
Institutions
- General Motors (United States) (US)
- Beijing Institute of Petrochemical Technology (CN)
- Tianjin University (CN)
- Hebei University of Technology (CN)
- Changsha University (CN)
Publication Details
- Journal
- IEEE Transactions on Industrial Electronics
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1109/tie.2024.3497310
- Citations
- 6
- Primary Topic
- Machine Fault Diagnosis Techniques
- Type
- article
- Field-Weighted Citation Impact
- 1.97