强噪声背景下的低频振荡模态辨识

Identification of Modes in Low Frequency Oscillation in Heavy Noise

传统Prony法存在着对噪声敏感的缺点,因此提出一种基于改进的整体平均经验模式分解(ensem-ble empirical mode decomposition,EEMD)方法滤波和改进Prony方法相结合的低频振荡分析方法。该方法先用改进的EEMD对低频振荡信号进行自适应滤波,再用改进Prony法对滤波后的信号进行分析。仿真结果表明:在较大噪声环境下,该方法仍能准确辨识出低频振荡模态参数。

Since traditional Prony analysis of low frequency oscillations is sensitive to the noise of data, this paper proposes an improved ensemble empirical mode decomposition filtering and improved Prony analysis combined method for low frequency oscillations analysis. In this method, improved ensemble empirical mode decomposition is used to adaptively filter the noise of the input signals before improved Prony analysis is carried out. The simulation shows that the proposed method can be accurate to abstract model parameters of low frequency oscillations in power system even in a heavy noise environment.