Adaptive White Noise Suppression Method for Partial Discharge Based on Wave Motions

YE Bin; ZHOU Kai; HUANG Yonglu; WANG Xianjin; ZHANG Qiaofeng; RAO Xianjie

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Adaptive White Noise Suppression Method for Partial Discharge Based on Wave Motions

更新时间：2025-12-08

- Adaptive White Noise Suppression Method for Partial Discharge Based on Wave Motions
- Vol. 47, Issue 2, Pages: 529-536(2021)
- 作者机构：
  
  四川大学电气工程学院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：28 February 2021，
  
  Published：2021
- 稿件说明：
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YE Bin, ZHOU Kai, HUANG Yonglu, et al. Adaptive White Noise Suppression Method for Partial Discharge Based on Wave Motions[J]. 2021, 47(2): 529-536.
DOI：

YE Bin, ZHOU Kai, HUANG Yonglu, et al. Adaptive White Noise Suppression Method for Partial Discharge Based on Wave Motions[J]. 2021, 47(2): 529-536. DOI：

摘要

局部放电（partial discharge

PD）检测是监测电缆绝缘状态的主要手段之一

然而现场PD测试时易受到白噪声干扰。针对该问题

提出一种基于波动（wave motions

WM）的局部放电白噪声自适应抑制方法。首先

该方法在时域上利用信号的波动特征实现白噪声的抑制

并利用时间阈值窗进一步消除冗余噪声。为了减少人为选值干扰

采用大津算法（OSTU）自适应选取阈值。其次

对MATLAB仿真的两种典型含噪PD信号进行去噪

并将去噪结果与离散小波变换（discrete wavelet transform

DWT）和自适应奇异值分解（adaptive singular value decomposition

ASVD）去噪结果进行对比

验证了该方法的可行性。最后

在实验室对典型电缆缺陷进行了工频局放测试

并对比DWT、ASVD及本方法对实测PD信号的去噪结果

验证了该方法的有效性。结果表明:相比DWT和ASVD法

该方法的去噪效果更优

算法耗时更短。

Abstract

Partial discharge（PD） detection is one of the main means of monitoring the insulation state of power cables. However

on-site PD testing is susceptible to white noise interference. An adaptive white noise suppression method for partial discharge based on wave motions（WM） is proposed to save this problem. Firstly

in this method

the fluctuation characteristics of signal in the time domain are utilized to suppress white noise white noise

and the time threshold window is utilized to further eliminate redundant noise. In order to reduce the interference of artificial selection

the Otsu algorithm（OSTU） is used to adaptively select the threshold. Secondly

the feasibility of the method is verified by two typical noisy partial discharge signals simulated on the MATLAB. The proposed method has been examined on simulated noisy PD signals

and its results have been compared with discrete wavelet transform（DWT） de-noised results and adaptive singular value decomposition（ASVD） de-noised results. Finally

the validity of the method is verified by PD tests on typical cable defects under AC voltage in the laboratory. The de-noised results of this method have been compared with DWT de-noised results

ASVD de-noised results

and the method de-noised results. The results show that this method has better denoising effect and shorter algorithm time compared with DWT and ASVD methods.

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Related Institution

Wuhan Power Supply Company,State Grid Hubei Electic Power Company

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Center for Post-doctoral Studies of Control Science and Engineering, Dalian University of Technology

Graduate School at Shenzhen, Tsinghua University

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