An Improved Denoising Method for Partial Discharge Signals Contaminated by White Noise Based on Adaptive Short-Time Singular Value Decomposition

Kai Zhou, Mingzhi Li, Yuan Li, Min Xie and Yonglu Huang
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Kai Zhou: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Mingzhi Li: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yuan Li: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Min Xie: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yonglu Huang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2019, vol. 12, issue 18, 1-16

Abstract: To extract partial discharge (PD) signals from white noise efficiently, this paper proposes a denoising method for PD signals, named adaptive short-time singular value decomposition (ASTSVD). First, a sliding window was moved along the time axis of a PD signal to cut a whole signal into segments with overlaps. The singular value decomposition (SVD) method was then applied to each segment to obtain its singular value sequence. The minimum description length (MDL) criterion was used to determine the number of effective singular values automatically. Then, the selected singular values of each signal segment were used to reconstruct the noise-free signal segment, from which the denoised PD signal was obtained. To evaluate ASTSVD, we applied ASTSVD and two other methods on simulated, laboratory-measured, and field-detected noisy PD signals, respectively. Compared to the other two methods, the denoised PD signals of ASTSVD contain less residual noise and exhibit smaller waveform distortion.

Keywords: partial discharge; denoising; singular value decomposition; minimum description length (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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