基于频率约束独立分量分析的开关柜局部放电故障识别  

Partial Discharge Fault Identification of Switchgear Based on Frequency Constrained Independent Component Analysis

作　　者：杨凯[1] 张认成[1] 杨建红[1] 杜建华[1] 樊真[1] 高艳艳[1] 

机构地区：[1]华侨大学机电及自动化学院,厦门361021

出　　处：《高电压技术》2014年第11期3452-3460,共9页High Voltage Engineering

基　　金：福建省产学合作科技重大项目(2012H6013;2012H6014);福建省自然科学基金(2012J01214);福建省科技计划重点项目(2013H0028)~~

摘　　要：针对开关柜局部放电故障检测中窄带干扰等多种噪声消除难题,为提高故障识别率,提出了一种新的局部放电故障识别方法。借助射频电流互感器和高速数据采集系统采集局部放电混合信号,运用频率约束独立分量分析方法从混合信号中分离局部放电的有效信号,克服了其它信号频谱混叠的影响,大幅度提高了信噪比。以0.4-1.1 MHz和1.8-2.7 MHz 2个频带的功率谱构造故障识别的特征向量,采用最小二乘支持向量机对局部放电故障进行识别。通过搭建局部放电实验平台验证了算法的有效性,实验结果表明,开关柜局部放电故障的识别率达98.0%,所提供的识别算法具有良好的泛化能力。In the switchgear partial discharge（PD） detection procedure, in order to improve the effectiveness of the fault identification under the conditions of the narrowband interference and other kinds of noise, which were difficult to eliminate, we developed a new PD identification method. PD mixed signals were acquired by radio frequency current transformers and high-speed data acquisition system. Then, the effective PD signals were separated from the mixed signals by frequency constrained independent component analysis（FCICA）. As the influence of other signals＇ aliasing was overcame, the signal-to-noise ratio was greatly improved. We used the separated signals＇ power spectra at the two frequency bands of 0.4-1.1 MHz and 1.8-2.7 MHz to construct characteristic vectors of fault identification, by which PD fault was identified by least squares support vector machine（LSSVM）.The validity of the developed methodology was verified through the PD experimental platform. Results show that the switchgear PD fault identification rate is up to 98.0% based on the developed method which has good ability of generalization.

关 键 词：局部放电 故障识别 支持向量机 约束独立分量分析 功率谱 开关柜 

分 类 号：TM591[电气工程—电器]