Application of White-noise Suppression in Gas Insulated Switchgear Partial Discharge Monitoring Based on Lifting Dual-tree Complex Wavelet Transform

YE Huisheng; CHEN Xiaolin; ZHOU Ting; DUAN Xiaoli; XIE Yaoheng; LI Xin

English Version Reader LoginJournal Login

您当前的位置：

首页 >

文章列表页 >

Application of White-noise Suppression in Gas Insulated Switchgear Partial Discharge Monitoring Based on Lifting Dual-tree Complex Wavelet Transform

更新时间：2025-12-08

- Application of White-noise Suppression in Gas Insulated Switchgear Partial Discharge Monitoring Based on Lifting Dual-tree Complex Wavelet Transform
- Vol. 43, Issue 3, Pages: 851-858(2017)
- 作者机构：
  
  1. 国网湖南省电力公司电力科学研究院
  2. 湖南大学电气与信息工程学院
  3. 国网湖南省电力公司
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：10 March 2017，
  
  Published：2017
- 稿件说明：
移动端阅览
YE Huisheng, CHEN Xiaolin, ZHOU Ting, et al. Application of White-noise Suppression in Gas Insulated Switchgear Partial Discharge Monitoring Based on Lifting Dual-tree Complex Wavelet Transform[J]. 2017, 43(3): 851-858.
DOI：

YE Huisheng, CHEN Xiaolin, ZHOU Ting, et al. Application of White-noise Suppression in Gas Insulated Switchgear Partial Discharge Monitoring Based on Lifting Dual-tree Complex Wavelet Transform[J]. 2017, 43(3): 851-858. DOI：

摘要

GIS局部放电在线监测过程中原本就微弱的放电信号往往会被白噪声所淹没

因此抑制白噪声是GIS局部放电在线监测工作中必不可少的环节。为了更加有效且快速地抑制局放在线监测中白噪声的干扰

针对第1代小波去噪算法存在着去噪效果不佳、计算效率低等不足之处

结合双树复小波变换良好的去噪能力和提升小波变换高效的计算能力

提出对双树复小波变换进行提升

得到第二代双树复小波变换

应用提升双树复小波变换进行白噪声抑制研究

并且在局部放电在线监测软件系统中引入该算法

应用于现场监测实验。仿真和实验结果表明

较之其他去噪算法

提升双树复小波变换算法的运算速度提高了83%

内存占用降低了将近50%

信噪比得到了提高

且波形相似系数更接近1

更能满足GIS局部放电在线监测的实时性要求

在工程实践中具有更高的应用价值。

Abstract

Weak partial discharge signals are always overwhelmed by white-noise in GIS partial discharge online monitoring.So how to suppress white-noise plays an important part in GIS partial discharge online monitoring.However

the first generation wavelet denoising algorithm has a large amount of calculation and poor denoising efficiency.In order to suppress white-noise more efficiently and quickly

we put forward a method that the lifting dual-tree complex wavelet is transformed into the white-noise suppression research because of the disadvantages of the first generation wavelet denoising algorithm.This algorithm is obtained by lifting the first generation dual-tree complex wavelet transform

and it combines the excellent denoising ability of complex wavelet transform and the high calculation efficiency of lifting wavelet transform.Then the online monitoring soft software system uses this algorithm as the core of calculation module to do field monitoring experiment.The results of simulation and experiment show that

compared with other denoising algorithms

the lifting dual-tree complex wavelet transform calculation speed is increased by 83%

memory occupancy is reduced by nearly 50 percent

the signal-to-noise ratio is improved

and waveform similarity coefficient is much closer to 1

which can better meet the the requirements of real-time in GIS partial discharge online monitoring

and has higher application value in the engineering practice.

关键词

Keywords

references

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Estimation Algorithm for Adaptive Threshold of Hybrid Particle Swarm Optimization Wavelet and Its Application in Partial Discharge Signals De-noising

超紧凑封装高压串联SiCMOSFET脉冲功率模块

Partial Discharge Detection Technology Based on Fiber Bragg Grating

Signal Reconstruction for Partial Discharge Electromagnetic Wave in Substation Based on Signal Model Parameters Identification

Diagnosis of Partial Discharge by Employing Inner Grading Electrode of Insulating Spacer in Gas Insulated Switchgear

Related Author

李清泉

秦冰阳

司雯

王若茜

刘宾

马帅

王霞

YAO Chenguo

Related Institution

School of Electrical Engineering Shandong University

Huazhong University of Science and Technology

Laiwu Power Supply Company, State Grid Shandong Electric Power Company

State Key Laboratory of Power Transmission Equipment Technology (School of Electrical Engineering, Chongqing University)

Microsystem and Terahertz Research Center, China Academy of Engineering Physics

AI问答

⁰