A New Partial Discharge Signal Denoising Algorithm Based on Adaptive Dual-Tree Complex Wavelet Transform

被引：68

作者：

Ghorat, Mohsen ^{[1
]}

Gharehpetian, G. B. ^{[1
]}

Latifi, Hamid ^{[2
,3
]}

Hejazi, Maryam A. ^{[4
]}

机构：

[1] Amirkabir Univ Technol, Dept Elect Engn, Tehran 158754413, Iran

[2] Shahid Beheshti Univ, Dept Phys, Tehran 1983969411, Iran

[3] Shahid Beheshti Univ, Laser & Plasma Inst, Tehran 1983969411, Iran

[4] Univ Kashan, Elect Engn Dept, Kashan 8731753153, Iran

来源：

IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT | 2018年 / 67卷 / 10期

关键词：

Denoising; partial discharge (PD); singular value decomposition (SVD); wavelet transform; SUPPRESSING WHITE-NOISE; THRESHOLD ESTIMATION; DECOMPOSITION; SELECTION; LOCATION;

D O I：

10.1109/TIM.2018.2816438

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Denoising is a key step in diagnosis and evaluation of partial discharge (PD) signals in power transformers. In this paper, a new PD signal denoising algorithm is presented, which is based on the combination of dual-tree complex wavelet transform (DTCWT) and adaptive singular value decomposition (ASVD). This new algorithm, which is introduced as adaptive DTCWT (ADTCWT), was evaluated through simulations and experimental tests. ADTCWT was employed in denoising from PD signals based on the selection of best singular values in each DTCWT level decomposition, corresponding to PD signal and noise. The superior performance of the ADTCWT algorithm was demonstrated using various indices in comparison with those of DTCWT and ASVD methods in noise reduction, besides preserving time of arrival of PD signals and PD localization.

引用

页码：2262 / 2272

页数：11

共 46 条

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