Partial discharge identification based on discharge amplitude-discharge frequency according to phase

ZHU Qingdong; ZHAO Xu; CHAI Qi; HU Jiaxi; ZHU Wenbing; ZHU Mengzhao; ZHOU Jiabin; XU Ran

doi:10.19666/j.rlfd.201706052

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Partial discharge identification based on discharge amplitude-discharge frequency according to phase

更新时间：2025-09-28

- Partial discharge identification based on discharge amplitude-discharge frequency according to phase
- Thermal Power Generation Vol. 47, Issue 2, Pages: 108-113(2018)
- 作者机构：
  
  1. 国网山东省电力公司电力科学研究院
  2. 西安热工研究院有限公司
  3. 利物浦大学科学与工程系
- 作者简介：
- 基金信息：
- DOI：10.19666/j.rlfd.201706052
  CLC： TM855
- Published Online：31 January 2018，
  
  Published：2018
- 稿件说明：
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ZHU Qingdong, ZHAO Xu, CHAI Qi, et al. Partial discharge identification based on discharge amplitude-discharge frequency according to phase[J]. Thermal power generation, 2018, 47(2): 108-113.
DOI：

ZHU Qingdong, ZHAO Xu, CHAI Qi, et al. Partial discharge identification based on discharge amplitude-discharge frequency according to phase[J]. Thermal power generation, 2018, 47(2): 108-113. DOI： 10.19666/j.rlfd.201706052.

摘要

针对局部放电物理特征的表示问题

本文提出采用放电幅值-放电次数联合相位分布特征表示局部放电物理特征。通过统计多个放电周期中每个放电相位上相同放电幅值发生的次数

得到局部放电二维统计矩阵;对该矩阵进行奇异值分解

得到放电幅值-放电次数联合相位分布特征;在该特征的基础上

利用支持向量机实现了局部放电识别。结果表明

该特征不但能够直观反映放电幅值-放电次数在工频相位上的分布特征

而且能够以较高的识别精度区分不同的放电类型

对尺寸大小不同的同一种放电类型有一定的识别能力。

Abstract

Some features extracted from partial discharge(PD) can not directly reflect the physical characteristics of discharge. To solve this problem

the distribution characteristics of discharge amplitude and discharge frequency according to frequency phase are proposed. In this paper

a two-dimensional statistical matrix of the partial discharge

named by partial discharge phase matrix(PPM)

is obtained by counting the number of the different discharge amplitudes on different frequency phases in the multiple frequency cycles. The singular value decomposition of the two-dimensional statistical matrix is used to get the distribution feature of discharge amplitude and discharge frequency according to phase

and support vector machine is introduced to realize the partial discharge identification. The results show that

this feature can not only reveal the distribution characteristics of discharge amplitude and frequency in the frequency phase

but also distinguish the different discharge patterns with higher recognition precision

and detect the same discharge type with different sizes.

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