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文章摘要
基于小波和高阶PDE的电缆局部放电去噪研究
Research on denoising of cables partial discharge based onwavelet and high order PDE
Received:December 29, 2016  Revised:December 29, 2016
DOI:
中文关键词: 局部放电  小波变换  高阶偏微分方程  信号去噪  
英文关键词: partial  discharge, wavelet  transform, higher-order  partial differential  equation, signal  de-noising
基金项目:
Author NameAffiliationE-mail
Wang Peipei* School of Electrical and Information Engineering,Jiangsu University 164771445@qq.com 
Mo Fujiang School of Electrical and Information Engineering,Jiangsu University mofujiang@ujs.edu.cn 
Zhao Ganggang School of Electrical and Information Engineering,Jiangsu University 110@qq.com 
Chen Hui School of Electrical and Information Engineering,Jiangsu University 1264665040@qq.com 
Xu Mengsu School of Electrical and Information Engineering,Jiangsu University 1424378993@qq.com 
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中文摘要:
      局部放电(PD)试验是检测电力电缆绝缘性能的重要手段,由于试验现场电磁环境比较复杂,提取所得的PD信号已被噪声淹没。为得到较为真实的PD信号,本文提出了一种基于小波变换和高阶偏微分方程(PDE)相结合的去噪方法。利用小波变换提供较好的局部放电信号预处理和后处理平台,对低频子信号进行四阶PDE迭代去噪,同时采用效果评价指数信噪比(SNR)作为迭代终止条件。将该方法的去噪效果与传统的小波阀值去噪效果进行了比较,经过计算和仿真分析表明,本文方法能够较好的保留信号边缘与细节,比传统小波阀值去噪具有更优越的去噪性能。
英文摘要:
      Partial discharge (PD) test is an important means to detect the insulation performance of power cable. Because the electromagnetic environment of the test site is complicated, the extracted PD signal is submerged by noise. In order to get a more real PD signal, the authors proposes a denoising method based on wavelet transform and high order partial differential equation (PDE). It uses wavelet transform to provide better pre-processing and post-processing platform of partial discharge signal, and utilizes the fourth-order PDE to iterate the low-frequency sub-signal. At the same time, the signal to noise ratio (SNR) is used as the iteration termination condition. The denoising effect of this method is compared with the traditional wavelet threshold denoising effect. Finally, the experimental results show that this method can suppress noise effectively and keep the property of edge and detail. It has better denoising performance than traditional wavelet threshold denoising method.
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