Prony方法和ARMA法在低频振荡模式辨识中的适用性比较

Prony方法和自回归滑动平均(ARMA)法是2种典型的电力系统低频振荡特征参数辨识方法,提出对这2种方法进行不同类型信号适用性的比较研究。在介绍2种方法基本原理的基础上,对比得出其在信号建模思想、模型参数估计原则上存在区别,进一步将这2种方法应用于处理36节点系统仿真明显扰动激励后系统响应信号和类噪声信号,对低频振荡模式辨识结果进行系统性研究。分析结果表明,ARMA法具有更好的适用性。

Comparison of applicability in low frequency oscillation mode identification between Prony and ARMA

Prony and ARMA(Auto Regressive Moving Average) are two typical methods of characteristic parameter identification used in the low frequency oscillation mode identification of power system. Based on the elementary principles,their applicability to different kinds of signals is comparatively assessed in signal modeling concept and model parameter estimation. For the systematic study of low frequency oscillation mode identification,these two methods are employed to process the simulative data,which are the response signals of 36-bus benchmark system after obvious disturbance excitation and the similar noise signals. The results of analysis show ARMA has better applicability than Prony.