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A Noise Suppression Method for Partial Discharge Based on Two-dimensional SVD

The analysis of PD measurements in the field is often hampered by electrical noise, which necessitates the application of sophisticated digital signal processing techniques. Aiming at the problem of noise suppression of partial discharge (PD) signals, this paper proposes a new de-noising method based on generalized S transformation and singular entropy incremental curvature spectrum. Firstly, the two-dimension time-frequency matrix of noise-contaminated PD signals is obtained by generalized S-transform, which is used as the trajectory matrix of singular value decomposition (SVD). then SVD is performed to calculate the singular entropy incremental curvature spectrum; A near-zero point is set to determine the threshold point to remove white noise; Finally, the denoised PD waveform is obtained by generalized S inverse transformation. The proposed method is applied to simulation signals and measured signals, and its results are compared with those of traditional wavelet denoising method and adaptive singular value decomposition (ASVD) denoising method. The results show that: compared with the other two methods, the denoising effect of the proposed method is better, and its results are more similar to the original PD signals.