Suppression of Narrow-band Noise of Partial Discharge Based on Improved SVD Algorithm

RAO Xianjie; ZHOU Kai; WANG Xianjin; LI Mingzhi; HUANG Yonglu; XIE Min

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Suppression of Narrow-band Noise of Partial Discharge Based on Improved SVD Algorithm

更新时间：2025-12-08

- Suppression of Narrow-band Noise of Partial Discharge Based on Improved SVD Algorithm
- Vol. 47, Issue 2, Pages: 705-713(2021)
- 作者机构：
  
  1. 四川大学电气工程学院
  2. 国网重庆市电力公司万州供电分公司
  3. 国网四川省电力公司技能培训中心
  4. 国网江苏省电力有限公司无锡供电公司
- 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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RAO Xianjie, ZHOU Kai, WANG Xianjin, et al. Suppression of Narrow-band Noise of Partial Discharge Based on Improved SVD Algorithm[J]. 2021, 47(2): 705-713.
DOI：

RAO Xianjie, ZHOU Kai, WANG Xianjin, et al. Suppression of Narrow-band Noise of Partial Discharge Based on Improved SVD Algorithm[J]. 2021, 47(2): 705-713. DOI：

摘要

为了解决阈值法和传统奇异值分解（singularvaluedecomposition

SVD）方法在局部放电（partialdischarge

PD）周期性窄带干扰抑制过程中存在的窄带干扰抑制不干净和PD波形畸变的现象

提出了一种带引导信号的SVD算法进行PD信号的窄带干扰抑制。首先结合PD混合信号快速傅里叶变换（fast Fourier transform

FFT）功率谱的特点

引入经典阈值用于判断窄带干扰数

并提出利用3谱线插值对窄带干扰频率进行精确估计;然后构造引导信号加入PD混合信号中

使得窄带干扰的奇异值和PD信号的奇异值便于分离

从而完成PD混合信号的窄带干扰抑制。仿真及实测结果表明

该方法可以有效地抑制周期性窄带干扰并能还原出原始的PD脉冲波形

同时通过和传统SVD方法和FFT阈值滤波对比说明了该方法的去噪效果更明显。

Abstract

To solve the waveform distortions and the bad performances of threshold methods and traditional singular value decomposition（SVD） methods for periodic narrow-band noise rejection of partial discharge（PD）

this paper presents a noise suppression method based on the improved SVD algorithm with guide signals. Firstly

the number of periodic narrow-band noise is determined by the classic threshold method combined with mixed PD signals fast Fourier transform（FFT） power spectrum

and a frequency estimation method based on three FFT sampled values is proposed to estimate the interference frequency accurately. Then the guide signals are structured and added to mixed PD signals to separate the singular value of narrow-band noise from that of PD signals

leading to suppression of narrow-band noise. The simulation and experiment results show that the proposed method can suppress narrow-band noise effectively and show the original PD signals waveform. Compared with traditional SVD methods and FFT denoising methods

the method proposed in this paper has better denoising results.

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