LAPSE:2023.13006v1
Published Article
LAPSE:2023.13006v1
RLS-Based Algorithm for Detecting Partial Demagnetization under Both Stationary and Nonstationary Conditions
Ze Jiang, Xiaoyan Huang, Wenping Cao
February 28, 2023
Abstract
An algorithm was developed detect the partial demagnetization of permanent-magnet synchronous motors (PMSMs) under both stationary and nonstationary conditions. On the basis of the recursive least-squares (RLS) method, the vital component of fault-related harmonics in the current could be extracted on the line, and its proportion to fundamental component could be regarded as the indicator of partial demagnetization faults. The proposed algorithm is fairly easy to realize and could substitute conventional and complicated signal processing methods such as Fourier transform and wavelet transform when detecting partial demagnetization. Experiments with inverter-fed healthy and partially demagnetized PMSMs are carried out to substantiate the effectiveness of proposed algorithm under both stationary and nonstationary conditions. At the end, a way to eliminate the impact of eccentricity fault on the partial demagnetization diagnosis is given.
Keywords
fault diagnosis, partial demagnetization, permanent-magnet synchronous motors (PMSMs), recursive least squares (RLS)
Suggested Citation
Jiang Z, Huang X, Cao W. RLS-Based Algorithm for Detecting Partial Demagnetization under Both Stationary and Nonstationary Conditions. (2023). LAPSE:2023.13006v1
Author Affiliations
Jiang Z: Zhejiang Provincial Key Laboratory of Electrical Machine Systems, College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; Wolong Electric Group Co., Ltd., Shaoxing 312300, China
Huang X: Zhejiang Provincial Key Laboratory of Electrical Machine Systems, College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
Cao W: School of Electrical Engineering and Automation, Anhui Unsiversity, Hefei 230039, China
Journal Name
Energies
Volume
15
Issue
10
First Page
3509
Year
2022
Publication Date
2022-05-11
ISSN
1996-1073
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Original Submission
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PII: en15103509, Publication Type: Journal Article
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LAPSE:2023.13006v1
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https://doi.org/10.3390/en15103509
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