Modelling Demagnetized Permanent Magnet Synchronous Generators using Permeance Network Model with Variable Flux Sources

Abstract

This paper aims to improve quadratic time-frequency distributions to adapt condition monitoring of electrical machines in transient states. Short-Time Fourier transform (STFT) has been a baseline signal processing technique for detecting fault characteristic frequencies. However, limits of window sizes due to loss of frequency-or time-resolution, make it hard to capture rapid changes in frequencies. Within this study, Choi-Williams and Wigner-Ville distributions are proposed to effectively detect peaks at characteristic frequencies while still maintaining low computation time. The improved quadratic timefrequency distributions allow for generating spectrograms of a longer lasting data signal and capturing multi-component signals with a better separation of the components than STFT. Further, the time resolution of the spectrograms generated by the proposed method is not affected by the window size. The effectiveness of the proposed methods is numerically verified from the data of an in-house test setup.