Advanced Signal Processing Techniques for Demagnetization Detection in PM Generators at Variable Speed

In this paper, demagnetization detection in permanent magnet generators is analyzed using advanced signal processing techniques. The t-f decomposition of the current signal is calculated using the min-norm method. Three signal processing techniques are investigated which are the adaptive demodulatio...

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Bibliographic Details
Published in:IEEE transactions on industry applications 2024-01, Vol.60 (1), p.1-10
Main Authors: Garcia-Calva, T. A., Gyftakis, K. N., Skarmoutsos, G. A., Mueller, M., Morinigo-Sotelo, D., Romero-Troncoso, R. de J.
Format: Article
Language:English
Subjects:
Adaptive signal processing
condition monitoring
Continuous wavelet transforms
Demagnetization
Demodulation
False alarms
Fault detection
fault diagnosis
Fourier transforms
Generators
permanent magnet machines
Permanent magnets
renewables
Resampling
Resonant frequencies
Signal processing
spectral analysis
Stator windings
Stators
Time-frequency analysis
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Summary:In this paper, demagnetization detection in permanent magnet generators is analyzed using advanced signal processing techniques. The t-f decomposition of the current signal is calculated using the min-norm method. Three signal processing techniques are investigated which are the adaptive demodulation, the time-varying resampling and the envelope analysis. The merits of these methods are thoroughly compared against the commonly adopted short-time Fourier transform analysis. Moreover, traditional signal processing techniques may not be suitable for fault detection when the machine operates at variable speed. Under such condition, interference between the fundamental frequency of the machine and the fault harmonic can lead to false alarms. Simulation test and experimental results demonstrate effective performance and feasibility of the proposed methods for demagnetization detection of permanent generators operating under non-stationary conditions.
ISSN:0093-9994
1939-9367
DOI:10.1109/TIA.2023.3312362