摘要
针对电力系统低频振荡辨识过程中广域测量信号经过滤波器产生色噪声干扰的问题,提出了一种基于矩阵协方差(COV)、二阶导数定阶法与总体最小二乘-旋转不变技术参数估计(TLS-ESPRIT)算法相结合的电力系统低频振荡辨识方法.首先将采集的信号通过带通滤波器去除趋势项和高频噪声,然后构造样本矩阵的协方差矩阵作为新的样本去除色噪声的影响,再通过TLS-ESPRIT算法进行模态辨识,在定阶问题上采用提出的二阶导数法定阶,使定阶具有自适应性,无须人为指定阈值.仿真结果表明:COV-TLS-ESPRIT算法相比其他方法具有抗噪性能好、拟合精度高等优点,可从噪声环境中准确地辨识出系统的主导模态,具有较强的实用性,能够实现在线辨识.
Aiming at the problem of colored noise interference generated by wide area measurement signal through the filter in process of low frequency oscillation identification in power system, a meth- od combined with covariance matrix, second order derivatives method and total least squares-estima- tion of signal parameters via rotational invariance techniques （TLS-ESPRIT） for identification of low frequency oscillation in power system was proposed. Firstly, signal was collected through the band- pass filter to remove trend and high frequency noise. Then, the covariance matrix of sample matrix was constructed as a new sample to eliminate the effect of colored noise. Finally, TLS-ESPRIT algo- rithm was used to identify modal parameters. The second order derivative method was adopted in the problem of determining order, making order adaptive, without artificially designating threshold. The simulation results show that compared with other methods, COV-TLS-ESPRIT algorithm has the ad- vantages of anti-noise performance and excellent fitting accuracy, which can effectively and accurately identify the dominant modes from environmental noise with strong practicability. This algorithm can also realize on-line identification conveniently.
出处
《华中科技大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2017年第4期90-96,共7页
Journal of Huazhong University of Science and Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(61304260)
福建省自然科学基金资助项目(2014J01169)
福建省教育厅资助项目(JA14215)
关键词
低频振荡
带通滤波器
协方差矩阵
模态辨识
拟合精度
low frequency oscillation
band-pass filter
covariance matrix
modal identification
fitting accuracy
