基于FastICA和Prony算法的低频振荡参数辨识

传统Prony算法进行参数辨识存在对信号噪声非常敏感的缺点,同时对输入信号有较高的要求。因此,本文首先介绍独立分量分析(Independent Component Analysis,即ICA)和FsatICA基本原理,然后提出将FastICA算法和Prony算法相结合的低频振荡参数辨识方法。该方法首先以广域测量信号作为输入信号,然后利用FastICA方法对输入信号进行预处理而达到降噪,最后利用Prony算法对滤波后的信号进行分析得到电力系统低频振荡参数。通过对理想信号和四机两区算例分析,验证了此方法在FastICA去噪之后,能够提高Prony提取低频振荡参数辨识的准确性、快速性和抗噪能力。

Parameter Identification of low frequency oscillation based on FastICA and Prony algorithm

Parameters are often identified by using Prony algorithm,but this method is sensitive to the noise of signals and has a high demand to the input signal.Therefore,the paper proposes a algorithm for identifying power system low frequency oscillation,which combines FastICA(Fast Independent Component Analysis) and Prony algorithm.First of all, wide area measurement signal is used as the input signal.And the preprocessing signal is denoised by ICA algorithm.In the end,the parameters of low frequency oscillation in the power system are obtained by using Prony algorithm,which analysis the denoised signal.Through analyzing the case studies on the ideal signal and four-machine system,it was found that the method improve the ability of accuracy,rapidity and anti-noise after the signal is denoised to identify the parameter of low frequency oscillation.