Optimized Denoising Method for Weak Acoustic Emission Signal in Partial Discharge Detection

被引：21
|
作者：
Lin, Qingcheng [1 ]
Lyu, Fuyong [1 ]
Yu, Shiqi [1 ]
Xiao, Hui [1 ]
Li, Xuefeng [1 ,2 ,3 ]
机构：
[1] Tongji Univ, Coll Elect & Informat Engn, Shanghai 201804, Peoples R China
[2] Tongji Univ, Frontiers Sci Ctr Intelligent Autonomous Syst, Shanghai 201210, Peoples R China
[3] Shenzhen Univ, Coll Phys & Optoelect Engn, Key Lab Optoelect Devices & Syst, Minist Educ & Guangdong Prov, Shenzhen 518060, Peoples R China
来源：
IEEE TRANSACTIONS ON DIELECTRICS AND ELECTRICAL INSULATION | 2022年 / 29卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Noise reduction; Gas insulation; Oscillators; Signal to noise ratio; Insulation; Wavelet analysis; Partial discharges; Denoising; gas-insulated switchgear (GIS); partial discharge (PD); pattern recognition; wavelet threshold function; DIAGNOSIS; INSULATOR;
D O I：
10.1109/TDEI.2022.3183662
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：
Acoustic emission (AE) technology can predict the occurrence of partial discharge (PD) faults, which is used to improve the safe operation level of gas-insulated switchgear (GIS) equipment. However, the strong noise interference from the production site is still the main factor affecting the identification accuracy. In this study, a simplified model is designed to approximate the accumulation of free metal particles on the surface of the GIS internal insulation structure, and white noise of various intensities is added to the collected PD-induced AE signals to simulate the background interference. The results prove that the proposed denoising method can achieve a better denoising effect in various signal-to-noise ratio (SNR) conditions. In particular, in the case of low SNR, the recognition accuracy of the accumulation degree of metal particles has been improved by more than 15%.
引用
收藏
页码：1409 / 1416
页数：8
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