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.
Highlights
Partial discharge (PD) detection has been widely employed to insulation condition monitoring of high-voltage equipment on-site
The denoising procedure is proposed, and the computational efficiency of the adaptive short-time singular value decomposition (ASTSVD) is discussed in detail
To show the performance of ASTSVD under high SNR, simulated PD signal s1 embedded in additive white Gaussian noise (AWGN) with SNR = 30 dB was filtered by ASTSVD and adaptive SVD (ASVD)
Summary
Partial discharge (PD) detection has been widely employed to insulation condition monitoring of high-voltage equipment on-site. Jin et al [12] used a novel method called novel adaptive EEMD (NAEEMD) to decrease the complexity and computational time of EEMD when white noise was superimposed multiple times in the original signal To prevent these deficiencies in EMD, methods based on SVD have been proposed [13,14]. The other important process in SVD is determining the number of effective singular values r, which will be used to reconstruct a noise-free signal. Besides determining proper window lengths and proper number of effective singular values, we can perform SVD segmentally to obtain better denoising results. To solve the problems above, a denoising method of PD signals named adaptive short-time singular value decomposition (ASTSVD) is proposed in this paper. The denoised results of ASTSVD were compared with the results of ASVD and a DWT-based denoising method
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