首页 | 本学科首页   官方微博 | 高级检索  
     

奇异值分解理论和小波变换结合的行波信号奇异点检测
引用本文:张峰,梁军,张利,贠志皓.奇异值分解理论和小波变换结合的行波信号奇异点检测[J].电力系统自动化,2008,32(20):57-60.
作者姓名:张峰  梁军  张利  贠志皓
作者单位:山东大学电气工程学院,山东省济南市,250061
基金项目:山东省自然科学基金资助项目(Y2006F14)
摘    要:准确检测故障行波信号的奇异点是行波故障测距的关键。现场故障行波信号通常含有大量噪声,有些情况下单独使用传统的小波变换将不能有效检测到信号的奇异点。为解决强噪声情况下故障行波信号奇异点的检测问题,提出了基于奇异值分解理论和小波变换的故障行波信号奇异点检测方法。通过构造重构的吸引子轨迹矩阵,并由Frobenious范数意义下的最佳逼近矩阵可以得到除噪后的信号序列,对所得信号序列进行奇异性检测得到信号序列奇异点。仿真结果表明,该方法在强噪声情况下可以去除噪声影响,并且保持信号的奇异性,准确检测到信号的奇异点。

关 键 词:奇异值分解  小波变换  行波  奇异点
收稿时间:3/30/2008 9:32:22 PM
修稿时间:2008/9/22 0:00:00

Traveling Wave Signal Processing Method for Singularity Detection Based on Singularity Value Decomposition and Wavelet Transform
ZHANG Feng,LIANG Jun,ZHANG Li,YUN Zhihao.Traveling Wave Signal Processing Method for Singularity Detection Based on Singularity Value Decomposition and Wavelet Transform[J].Automation of Electric Power Systems,2008,32(20):57-60.
Authors:ZHANG Feng  LIANG Jun  ZHANG Li  YUN Zhihao
Abstract:It is the crucial problem to accurately detect the traveling wave singularity point in fault location.Much noise is usually contained in the traveling wave signal of field data,in which case,the singularity point cannot always be detected using the conventional wavelet transform.Accordingly,a traveling wave signal processing method for singularity detection based on singularity value decomposition and wavelet transform is proposed.After the track matrix of an attractor reconstructed by time series is structured,a signal series without noise will be obtained by the optimal approximation matrix in the Frobenious norm,and the singularity point will be detected in the noise cancelled signal series.The simulation result shows that the method can maintain the singularity characteristic and accurately detect the singularity point in the noise background.
Keywords:singularity value decomposition  wavelet transform  traveling wave  singularity point
本文献已被 CNKI 万方数据 等数据库收录!
点击此处可从《电力系统自动化》浏览原始摘要信息
点击此处可从《电力系统自动化》下载全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号