Analysis of Joint Time-frequency Analysis on Partial Discharge Signal Based on Improved EEMD and Cohen’s Class

YANG Fengyuan; SONG Hui; CHENG Xu; DUAN Dapeng; TAO Shiyang; HOU Huijuan

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Analysis of Joint Time-frequency Analysis on Partial Discharge Signal Based on Improved EEMD and Cohen’s Class

更新时间：2025-12-08

- Analysis of Joint Time-frequency Analysis on Partial Discharge Signal Based on Improved EEMD and Cohen’s Class
- Vol. 42, Issue 7, Pages: 2345-2352(2016)
- 作者机构：
  
  1. 上海交通大学电气工程系
  2. 国网北京电力科学研究院
- 作者简介：
- 基金信息：
- DOI：
  CLC：
- Published Online：31 July 2016，
  
  Published：2016
- 稿件说明：
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YANG Fengyuan, SONG Hui, CHENG Xu, et al. Analysis of Joint Time-frequency Analysis on Partial Discharge Signal Based on Improved EEMD and Cohen’s Class[J]. 2016, 42(7): 2345-2352.
DOI：

YANG Fengyuan, SONG Hui, CHENG Xu, et al. Analysis of Joint Time-frequency Analysis on Partial Discharge Signal Based on Improved EEMD and Cohen’s Class[J]. 2016, 42(7): 2345-2352. DOI：

摘要

局部放电信号特征提取及模式识别是电力设备状态评估和故障诊断的关键

基于局部放电波形的时频分析能够全面获取波形特征

但目前常用的时频分析方法存在交叉项干扰问题

会产生"虚假信号"。为解决该问题

提出将集合经验模态分解（EEMD）和Cohen类分布结合的方法应用于局部放电信号的时频分析

并运用回归型支持向量机（SVR）抑制EEMD端点效应。利用指数衰减振荡函数加入Gaussian白噪声和窄带干扰来模拟电力设备高频电流局放信号。结果表明

该方法能够准确辨识出特征脉冲信号

且在保证信号项时频聚集性的同时

有效削弱信号内部交叉干扰项。利用该方法对变电站现场实测局放信号进行时频分析

所得结果验证了其在现场干扰环境下的有效性和实用性。

Abstract

The feature extraction and pattern recognition of partial discharge signal are key steps of equipment condition assessment and fault diagnosis. Time-frequency analysis on PD pulse can extract more comprehensive and effective information from waveform. However

the cross interference terms have the influence on the commonly used timefrequency analysis method. Consequently

we put forward a method of joint time-frequency analysis on PD pulse signal based on EEMD and Cohen’s class

studied the end effect of EEMD

and proposed an extending technology based on SVR-regression fitting method. Moreover

the exponential attenuation oscillating function added with Gaussian white noise and narrow band interference was used to simulate the high frequency current PD signal of power equipment. The results show that this method can accurately identify the characteristic PD pulse. It can not only guarantee the time-frequency concentration of effective signal

but also inhibit the influence of IMFs’ cross interference terms. Finally

we prove the effectiveness and practicality of this method by applying it to analyze the PD signal which is measured in substation.

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Related Institution

Wuhan Guoce Hengtong Intelligent Instrument Co., Ltd.

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