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。
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.
出处
《电力系统自动化》
EI
CSCD
北大核心
2008年第20期57-60,共4页
Automation of Electric Power Systems
基金
山东省自然科学基金资助项目(Y2006F14)~~
关键词
奇异值分解
小波变换
行波
奇异点
singularity value decomposition
wavelet transform
traveling wave
singularity point
