基于改进去噪性能的Prony算法电网低频振荡模态辨识研究

针对电网低频振荡Prony辨识算法对噪声较为敏感、对输入信号要求较高的问题,提出了一种基于小波去噪与扩展Prony算法相结合的高精度低频振荡模态辨识方法。在小波去噪的基础上通过对阈值进行改进,使得小波去噪的阈值随着小波的分解而发生变化,从而对低频振荡信号达到较好的滤波效果,并在此基础上研究扩展Prony算法,对构建的仿真信号运用IEEE4机2区域系统产生低频振荡信号以及实际PMU监测的低频振荡信号进行算法验证。仿真和实验表明提出的方法能够比较准确和快速的辨识电力系统低频振荡信号,且具有较高的精度和较好的鲁棒性,为电力系统低频振荡模态辨识提供了一种行之有效的方法。

Power grid low frequency oscillation recognition based on advanced Prony algorithm with improved denoising feature

A method based on wavelet denoise and improved Prony algorithm were proposed to identify low frequency oscillation modes,in view of the fact the Prony algorithm is very sensitive to noise and high requirements for the input signal.On the basis of wavelet denoising analysis,the threshold technique was improved,and the wavelet threshold was made changing with wavelet decomposition.Then an improved Prony algorithm were researched to recognize the mode of low-frequency oscillation.Through building signal,IEEE 4 machine 2 area system simulation and experiments,the proposed wavelet denoising method and Prony algorithm were proved to have a high accuracy and good robust performance.It is very feasibility to deal with the noise of power system and can identify low frequency oscillation modes rapidly and accurately in a certain noise environment,and provide an effective method to power system low-frequency oscillation.