直流局部放电脉冲峰值—时间序列特征指纹提取  

Feature Extraction for Peak-time Series of Partial Discharge at HVDC

作　　者：司文荣[1] 傅晨钊[1] 黄华[1] 李军浩[2] 李彦明[2] 

机构地区：[1]上海市电力公司电力科学研究院,上海200437 [2]西安交通大学电力设备电气绝缘国家重点实验室,西安710049

出　　处：《高压电器》2013年第11期17-24,共8页High Voltage Apparatus

基　　金：国家自然科学基金资助(50977075)~~

摘　　要：在简述直流下局部放电试验系统以及用于直流局部放电试验的空气中电晕、油纸绝缘的尖板、内部缺陷和沿面放电4种典型局部放电缺陷模型之后,利用自行研制的直流局部放电宽带检测系统获取了缺陷模型在直流电压下的大量局部放电脉冲波形—时间序列。并对获取的脉冲波形—时间序列,使用基于波形时频特征的脉冲群快速分类技术进行随机干扰脉冲剔除,从而获得直流局部放电脉冲峰值—时间序列。在上述工作基础之上,引入Delta(t)参数,把交流局部放电识别方法运用到直流局部放电。对选用的部分直流局部放电TARPD谱图,使用统计算子计算得到36个放电指纹参数。基于遗传算法优化的BP神经网络(GA-BP)对放电指纹参数进行特征评价和选取。研究表明,保留20个有效指纹参数即可达到区分放电类型的目的。Three basic defect models of oil-paper insulation including corona, bounded cavity and surface discharge, and a typical discharge model of corona in air are described after the DC PD test and measurement system had been shown. The PD measurement system has a special anti-noise technique which resorts to the characteristic of pulse waveshape signals in time and frequency domain to achieve fast separation of random pulsed noise signals. Based on those works done above, the parameter Delta（t） is introduced for DCPD to form a new analysis approach, which perform the same function as the popular method named phase resolved partial discharge （PRPD）histograms of PD at AC voltage. Thirty-six fingerprints of DCPD are gained resorting to application of the standard statistic operators to some selected time and amplitude resolved partial discharge （TARPD）histograms. Then, feature selection are achieved by using a BP Neural Network optimized by genetic algorithm （GA-BP）, while the result is that only twenty optimized fingerprints are reserved.

关 键 词：直流 局部放电 脉冲峰值-时间序列 特征提取 遗传神经网络 

分 类 号：TM835[电气工程—高电压与绝缘技术]