基于窗口滑动改进Prony算法的电力系统低频振荡识别

A Moving-window Prony Algorithm for Power System Low Frequency Oscillation Identification

Prony算法是获取电力系统低频振荡信息的有效方法。针对传统Prony方法只能选取局部信号进行分析且对噪声敏感的问题,提出了固定窗长滑动的改进措施,进而应用于电力系统低频振荡识别中。与传统方法相比,改进的Prony方法通过对信号采取滑动窗口处理,即将拟合目标函数由局部误差平方最小变为分窗口误差平方和最小,从而能够计及较长时间内的误差平均效应,因而具有抗噪声能力强、不受信号长度影响的特点。基于DSATools的仿真分析证实了窗口滑动的改进Prony算法能有效识别低频振荡模式,并且该方法在含噪声环境下仍然能够保持较高的准确性。

Prony algorithm is an effective method to derive characteristics of power system low frequency oscillations. However, the traditional Prony algorithms are very sensitive to noises and can only be applied to part of signal every time. This paper proposes the moving-window Prony algorithm to the identification of low frequency oscillation of power system. With the moving-window technique, the objective function of the proposed Prony method is changed into the least square error by windows, which is different from the partial least square error corresponding to the traditional algorithms. Thus, the moving window Prony algorithm has stronger adaptability to noise and could be applied to long time range signals. The simulation results based on DSA Tools demonstrate that the moving window Prony algorithm has good and effective performance for identification of power system low frequency oscillation, even in cases when the PMU＇s data contains noises.