基于CCF-TLS-ESPRIT算法的低频振荡在线辨识

低频振荡在线辨识需用到广域测量系统(WAMS)的采集信号,而WAMS采集的信号中常伴有高斯白噪声,经过常规的低通滤波处理后会产生高斯色噪声,从而影响辨识的精度.针对在线辨识中的色噪声问题,提出以互相关函数(CCF)来代替实测信号,从而抑制色噪声,并结合总体最小二乘-旋转不变技术参数估计(TLS-ESPRIT)算法进行模态辨识.仿真结果表明,CCF-TLS-ESPRIT算法能够有效辨识出色噪声环境中的系统低频振荡模态,并具有一定的效率.

On-line identification for low frequency oscillation based on CCF-TLS-ESPRIT algorithm

On-line identification for low frequency oscillation needs to measure signals which couple with white Gaussian noise from wide area monitoring systems (WAMSs). Processed by low pass filter, white Gaussian noise can turn into colored Gaussian noise, so the accuracy of oscillatory indentification would be reduced. To solve the problem of colored Gaussian noise, in this paper we propose a cross-correlation-function (CCF) method that could reduce the influence of colored Gaussian noise. Combined with TLS-ESPRIT algorithm, CCF-TLS-ESPRIT could identify oscillatory modes in the environment of colored Gaussian noise rapidly. The simulation results show the effectiveness of the proposed method.
Keywords： wide area monitoring systems cross-correlation-function TLS-ESPRIT colored Gaussian noise