基于时频流形的水轮发电机组局放信号特征提取方法  

Feature extraction of generator partial discharge signals using time-frequency manifolds

作　　者：武桦[1] 贾嵘[1] 罗兴锜[1] 谢永涛[1] 

机构地区：[1]西安理工大学水利水电学院,西安710048

出　　处：《水力发电学报》2016年第9期55-62,共8页Journal of Hydroelectric Engineering

基　　金：国家自然科学基金(51279161)

摘　　要：准确地提取水轮发电机机组局部放电信号特征,对于发电机绝缘在线监测具有重要意义。为此,本文提出了基于时频流形的发电机局部放电信号特征提取方法。首先通过相空间重构算法,将局部放电(PD)时域信号转换为多个子序列,并分别求其时频分布,构建PD信号的时频流形。然后利用局部线性嵌入算法(LLE)将高维数据映射到低维空间,提取PD信号在低维空间的特征参数。最后,通过K-最近邻分类器(KNNC)的故障诊断模型实现发电机组不同局部放电的模式识别,其故障识别率高于95%。Accurate extract of signal features of partial discharge （PD） is crucial to on-line monitoring of generator set insulation systems. This paper describes a new extraction method of the PD signals based on time-frequency manifolds. This method uses phase space reconstruction to convert a PD signal into multiple sub-sequences, calculates their respective time-frequency distributions, and constructs dynamic time-frequency manifolds of the raw PD signal. Then, using locally linear embedding, the high-dimensional data are mapped to a low dimensional space where feature parameters of the PD signals are extracted. The new method has been applied to identification of PD patterns of different generators using a K-nearest neighbor classifier （KNNC）. Its failure recognition rate is higher than 95%.

关 键 词：发电机 局放 时频流形 流形学习 局部线性嵌入算法 

分 类 号：TM81[电气工程—高电压与绝缘技术]