基于T-F聚类和PRPD图谱分析的配网电缆局部放电类型识别研究*

doi:10.11985/2022.02.027

摘要/Abstract

摘要：

对配网电电缆中的局部放电类型进行识别对于监测电缆运行状态、提升电缆寿命具有重要意义,然而用于模式识别的指纹库一般都是基于单一缺陷构造的。当存在随机干扰源或多局放源时,基于传统的脉冲幅值-时间序列的宽带检测系统在放电模式的识别过程中会出现误判情况。针对配网电缆的局部放电缺陷,提出一种利用等效时频分析、模糊C均值聚类结合PRPD谱图实现对局部放电类型的分类识别方法,该方法首先根据脉冲波形特征值进行分类,之后通过PRPD图谱进行识别,可有效解决传统直接识别方法无法正确判断多局放源的问题。首先建立了局部放电测试系统,采集测量得到不同类型的局部放电波形。然后通过等效时频分析,将原始放电的时域波形转化为T-F模式,之后采用模糊C均值聚类分析将T-F模式下的数据分类,对每一类放电脉冲提取PRPD图谱识别其放电类型,实现配网电缆多放电源局放信号的准确分类与识别。试验结果表明,在多个单一局放源和两种混合局放源放电情况下该方法能够对其放电类型进行有效的分类和识别。

关键词: 局部放电, 多放电源, 放电模式识别, 相位谱图

Abstract:

It is important to identify partial discharge(PD) type in distribution cable for monitoring cable running state and extending cable life. The fingerprint library used for pattern recognition is generally constructed based on a single defect. If we detect a PD signal based on the traditional pulse amplitude-time series broadband detection system when there is random interference source or multi-station discharge source, misjudgment will occur in the process of pattern recognition. Aiming at pd defects of distribution cables, a method of pd classification and recognition is proposed based on equivalent time-frequency analysis, fuzzy C-means clustering and PRPD spectrum. The pulse waveform is firstly classified according to its characteristic value, and then is identified by PRPD spectrum, which can effectively solve the problem that the traditional direct identification method cannot correctly judge the multi-local discharge source. Firstly, a partial discharge testing system is established, and different types of partial discharge waveforms are obtained. Then, the time-domain waveform of the original discharge is transformed into T-F mode by equivalent time-frequency analysis, and the data in T-F mode is classified by fuzzy C-means clustering analysis, and the PRPD spectrum of each type of discharge pulse is extracted. Finally, the discharge type is identified according to the PRPD spectrum, and the accurate classification and identification of the local discharge signals of the multi-discharge power supply in distribution cables is realized. Experimental results show that this method can classify and identify discharge types effectively in the case of multiple single local discharge sources and two mixed local discharge sources.

Key words: Partial discharge, multi-discharge power, type identification of discharge, PRPD

中图分类号:

TM85

周达, 张昕, 邹云峰, 倪玉玲, 王德玉. 基于T-F聚类和PRPD图谱分析的配网电缆局部放电类型识别研究^*[J]. 电气工程学报, 2022, 17(2): 235-242.

ZHOU Da, ZHANG Xin, ZOU Yunfeng, NI Yuling, WANG Deyu. Study on Partial Discharge Pattern Recognition for Distribution Cable Based on T-F Clustering and PRPD Spectrum Analysis[J]. Journal of Electrical Engineering, 2022, 17(2): 235-242.

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