Partial discharge de-noising employing adaptive singular value decomposition

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

“…In ASTSVD, it is notable that signals are processed segmentally through applying a sliding window to the PD signal. Consequently, when we deal with a PD signal with large data, the computational efficiency of ASTSVD is much higher than that of the methods which performs SVD on the whole PD signal, such as ASVD [26]. This will be further verified in the next part.…”

“…When processing PD signals with a large amount of data, ASVD [26] requires a great deal of memory and much computational time, limiting its applicability. However, by applying a sliding window, ASTSVD can reduce both the memory requirements and the computational requirements.…”

“…Then, we mixed the sinusoidal signals together to obtain a mixed signal which contained five signal components with different frequency. ASVD [26] and MDL were used to identify r of the mixed signal, which ought to be 10. The results of singular value selection under different SNR is shown in Figure 4.…”

“…One of them is embedding the signal into a L × K matrix. Therefore, one of the key problems concerned in SVD is determining the window length L. According to previous study, L should be between N/20 [26] and N/2 [27], where N was the total amount of signal points. Elsner and Tsonis [28] remarked that choosing L = N/4 is a common practice.…”

“…SVD was first used in denoising of PD signals by Abdel-Galil et al [13]. However, conventional SVD cannot work well in some cases due to human intervention in selecting the number of effective singular values r. Consequently, Ashtiani et al [26] proposed adaptive SVD (ASVD) to determine r by finding out the first nonzero floor function of the standard deviation for each singular value subset. By now, researchers still focus on processing a whole PD signal for once.…”