Analysis of the partial discharge of ultrasonic signals in large motor based on Hilbert-Huang transform

Abstract

Partial discharge (PD) is one of the main diagnosis methods for the insulation aging of large motors. A data compression denoising method based on the empirical mode decomposition (EMD) algorithm is proposed according to the weak and large disturbances of large motor PD signals. A simulation model is constructed to verify the effectiveness of the proposed algorithm. Furthermore, the denoising effect of the proposed algorithm is compared with that of the db2 and db8 wavelets in the experiments. The simulation and experimental results show that a data compression denoising algorithm based on EMD can achieve the same denoising effect with those based on db2 and db8 wavelets. The proposed algorithm is even better in terms of relative error and mean square error. For actual signal processing, the data compression denoising algorithm is better than the other algorithms and does not lose the original signal energy. The PD ultrasonic signals are spectrally analyzed by using HHT, and signal energy distribution in time and frequency can be clearly described. The main PD ultrasonic signal characteristics can be extracted currently from the marginal and Hilbert spectra. The results of this study will be helpful for the diagnosis of PD faults in large motors.