scholarly journals Suppression Method of Partial Discharge Interferences Based on Singular Value Decomposition and Improved Empirical Mode Decomposition

Energies ◽  
2021 ◽  
Vol 14 (24) ◽  
pp. 8579
Author(s):  
Linao Li ◽  
Xinlao Wei
Keyword(s):  
Singular Value Decomposition ◽  
White Noise ◽  
Partial Discharge ◽  
Singular Value ◽  
Narrowband Interference ◽  
Noise Interference ◽  
Mode Decomposition ◽  
Interference Signals ◽  
Suppression Method ◽  
Value Decomposition

Partial discharge detection is an important means of insulation diagnosis of electrical equipment. To effectively suppress the periodic narrowband and white noise interferences in the process of partial discharge detection, a partial discharge interference suppression method based on singular value decomposition (SVD) and improved empirical mode decomposition (IEMD) is proposed in this paper. First, the partial discharge signal with periodic narrowband interference and white noise interference x(t) is decomposed by SVD. According to the distribution characteristics of single values of periodic narrowband interference signals, the singular value corresponding to periodic narrowband interference is set to zero, and the signal is reconstructed to eliminate the periodic narrowband interference in x(t). IEMD is then performed on x(t). Intrinsic mode function (IMF) is obtained by EMD, and based on the improved 3σ criterion, the obtained IMF components are statistically processed and reconstructed to suppress the influence of white noise interference. The methods proposed in this paper, SVD and SVD + EMD, are applied to process the partial discharge simulation signal and partial discharge measurement signal, respectively. We calculated the signal-to-noise ratio, normalized correlation coefficient, and mean square error of the three methods, respectively, and the results show that the proposed method suppresses the periodic narrowband and white noise interference signals in partial discharge more effectively than the other two methods.

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

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3465 ◽  
Author(s):  
Kai Zhou ◽  
Mingzhi Li ◽  
Yuan Li ◽  
Min Xie ◽  
Yonglu Huang
Keyword(s):  
Singular Value Decomposition ◽  
White Noise ◽  
Minimum Description Length ◽  
Partial Discharge ◽  
Singular Values ◽  
Singular Value ◽  
Denoising Method ◽  
Signal Segment ◽  
Value Decomposition ◽  
Short Time

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.

Denoising Lidar Signal Based on Ensemble Empirical Mode Decomposition and Singular Value Decomposition

2017 ◽  
Vol 46 (12) ◽  
pp. 1201003
Author(s):  
程知 CHENG Zhi ◽  
何枫 HE Feng ◽  
靖旭 JING Xu ◽  
张巳龙 ZHANG Si-long ◽  
侯再红 HOU Zai-hong
Keyword(s):  
Singular Value Decomposition ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Singular Value ◽  
Lidar Signal ◽  
Mode Decomposition ◽  
Value Decomposition

Modeling Research of Background Noise in Low-Voltage Power Line Communication Channel

2012 ◽  
Vol 516-517 ◽  
pp. 1386-1390 ◽  
Author(s):  
Hao Kun Guo ◽  
Jun Ji Wu ◽  
Zhan Feng Ying
Keyword(s):  
Singular Value Decomposition ◽  
Background Noise ◽  
Low Voltage ◽  
Power Line ◽  
Singular Value ◽  
Ar Model ◽  
Recursive Method ◽  
Noise Interference ◽  
Set Up ◽  
Value Decomposition

Background noise interference is one of the most important factors for low-voltage power line communication’s reliability. By analyzing the background noise of low-voltage power line communication’s channel, the background noise’s measuring circuit is set up and the AR model of the measured background noise is established. Both of them are respectively using singular value decomposition and Levinson-Durbin (LD) recursive method to calculate the AR model’s parameters and a comparative analysis of the simulation is made. The results induct: parameters acquired from the methods of singular value decomposition and LD recursive method are feasible, the parameter model from singular value decomposition is relatively complex, but extremely accurate, which is suitable for the off-line calculation and analysis of the low-voltage power line’s background noise; the parameter model from LD recursive method is very simple, but has a greater loss of accuracy, fitting for online quickly generation of the low-voltage power line’s background noise.

Microseismic denoising assessment by polarization histograms

Geophysics ◽  
2020 ◽  
pp. 1-46
Author(s):  
German I. Brunini ◽  
Juan I. Sabbione ◽  
Julián L. Gómez ◽  
Danilo R. Velis
Keyword(s):  
Singular Value Decomposition ◽  
Quality Factor ◽  
Field Data ◽  
Synthetic Data ◽  
Singular Value ◽  
Mode Decomposition ◽  
Actual Field ◽  
Band Pass ◽  
Value Decomposition ◽  
Better Than

We present a comparison of microseismic data denoising methods based on their effect on the polarization attributes of 3C microseismic signals. The compared denoising methods include the classical band-pass filtering, and three recently proposed denoising techniques: restricted domain hyperbolic Radon transform denoising, singular value decomposition-based reduced-rank filtering, and empirical mode decomposition denoising. In order to draw the comparison, we have denoised 3C synthetic data contaminated with noise extracted from actual field data records, calculated their rectilinearity, azimuth, and dip polarization attributes, and arranged them into histograms. The comparison has been drawn by measuring the distances between the polarization histograms of the clean and denoised data, assuming that one method outperforms another if the aforementioned distance is smaller. This strategy allows to quantify the improvement in the calculated polarization attributes due to the different denoising processes. In addition, we have also calculated the quality factor of the denoised signals, which adds value and robustness to the comparison. Our results have indicated that the method based on singular value decomposition preserves the original polarization attributes better than the other techniques tested in this work. Moreover, it has also retrieved the denoised signal with the highest quality factor. Finally, we have tested the methods with field data and assessed their performance qualitatively on the basis of the insight gained from the numerical tests with synthetic data.

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