摘要
提出了用短时傅里叶变换作为时频信号分析工具研究电能质量扰动识别问题,同时提出了用奇异值分解技术研究扰动时间定位问题。从扰动电压信号短时傅里叶变换后得到的二维频谱幅值矩阵中提取出4个特征序列,生成频谱峰值曲线、基频幅值曲线、高频幅值曲线和幅值标准差曲线,这些曲线用6个特征量来表征。当6个特征量中的某几个满足一定的取值组合时能够唯一确定一种扰动,文中通过建立决策树,实现多种单一与复合扰动的识别。利用采样信号构造Hankel矩阵,对此矩阵进行奇异值分解,通过分量信号的构造并从中提取模极大值点,进行扰动时间定位。仿真结果表明,本文提出的方法能够实现8种单一与8种复合扰动的类型识别,准确检测出电压暂降、暂升、中断的幅值,并可对电压暂降、振荡、脉冲等扰动进行精确的扰动时间定位。
It is proposed to identify power quality disturbances by using short-term Fourier transform as the analysis tool of time-domain signals, meanwhile the problem to research the time orientation of disturbance by singular value decomposition is put forward. Four feature sequences are extracted from the two-dimensional frequency spectrum amplitude matrix obtained from the short-term Fourier transform of disturbance voltage signals to create four curves, i.e., the curve of spectrum peak values, the curve of fundamental frequency amplitudes, the curve of high-frequency amplitudes and the standard deviation curve of amplitudes, and these curves are characterized by six characteristic quantities respectively. When several of six characteristic quantities conform to a certain combination of taking value, a kind of disturbance can be determined exclusively. In this paper, by means of setting up decision-making tree the identification of single disturbance and compound disturbance is implemented. Constructing Hankel matrix by use of sampled signals and performing singular value decomposition of the Hankel matrix, the orientation of disturbance time can be realized by constructing component signals and extracting modulus maximum point from the component signals. Simulation results show that using the proposed method, the type recognition of eight kinds of singular disturbances and eight kinds of compound disturbances can be implemented, and the amplitudes of voltage sag, swell and interruption can be accurately measured, besides, the time orientation of such disturbances as voltage sag, oscillation and pulses can be accurately implemented.
出处
《电网技术》
EI
CSCD
北大核心
2011年第8期174-180,共7页
Power System Technology
关键词
短时傅里叶变换
奇异值分解
扰动识别
扰动时间定位
short-time Fourier transform
singular value decomposition
disturbance identification
orientation of disturbance time