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On-line Detection and Classification of PMSM Stator Winding Faults Based on Stator Current Symmetrical Components Analysis and the KNN Algorithm
Open accessAbstract
The significant advantages of permanent magnet synchronous motors, such as very good dynamic properties, high efficiency and power density, have led to their frequent use in many drive systems today. However, like other types of electric motors, they are exposed to various types of faults, including stator winding faults. Stator winding faults are mainly inter-turn short circuits and are among the most common faults in electric motors. In this paper, the possibility of using the spectral analysis of symmetrical current components to extract fault symptoms and the machine-learning-based K-Nearest Neighbors (KNN) algorithm for the detection and classification of the PMSM stator winding fault is presented. The impact of the key parameters of this classifier on the effectiveness of stator winding fault detection and classification is presented and discussed in detail, which has not been researched in the literature so far. The proposed solution was verified experimentally using a 2.5 kW PMSM, the construction of which was specially prepared for carrying out controlled inter-turn short circuits.
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In stator winding fault, the amplitude of the fs component in the current negative sequence component increases significantly, while the positive sequence component is not sensitive.
“The authors have proven that in the case of the stator winding fault, the amplitude of the fs component in the current negative sequence component increases significantly, while the positive sequence component is not sensitive to this type of failure.”
From Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges (2022) · cites this paper as [48]
Section 3 reports that the fs amplitude increases after an inter-turn short circuit and that the increase is greater in the negative-sequence component. It also reports an increase in the positive-sequence component and uses its amplitude as a fault-classifier feature, so the claim that the positive sequence is not sensitive is unsupported. Page numbers are not available in the supplied passages. (LLM, conf=0.90)
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Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges
Orlowska-Kowalska, T., Wolkiewicz, M., Pietrzak, P., Skowron, M., Ewert, P., Tarchala, G., Krzysztofiak, M., & Kowalski, C. T. (2022) · IEEE Access · doi:10.1109/access.2022.3180153 · Cited by 328
Self-citation
Metadata
- Authors
- Pietrzak, P., & Wolkiewicz, M.
- Affiliations
- Department of Electrical Machines, Drives and Measurements, Wroclaw University of Science and Technology, Wybrzeze Wyspianskiego 27, 50-370 Wroclaw, Poland
- Wrocław University of Science and Technology (PL)
- Corresponding author
- Wolkiewicz, Marcin
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2021-07-26
- Volume
- 10
- Issue
- 15
- Pages
- 1786
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3184858403
- Cited by
- 54
- References
- 63
- Citations by year
- 2026: 2 · 2025: 13 · 2024: 10 · 2023: 14 · 2022: 12 · 2021: 3
- FWCI
- 4.34
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Narodowe Centrum Nauki (2017/27/B/ST7/00816)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Motor Design and Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Sensorless Control of Electric Motors
- Keywords
- permanent magnet synchronous motor, stator winding faults, inter-turn short circuit, fault detection, fault classification, spectral analysis, k-nearest neighbors, machine learning
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