基于经验模态分解滤波的低频振荡Prony分析

传统Prony法在分析低频振荡时对输入信号要求较高，存在着对噪声敏感的弱点.因此提出一种经验模态分解滤波和改进Prony法相结合的低频振荡分析方法.该方法先用经验模态分解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.其中，改进Prony法有效阶数用归一化奇异值法确定.将该方法分别用于分析试验信号和IEEE 4机系统振荡信号，并与基于低通滤波器的Prony分析进行比较.结果表明，在较大噪声环境下，该方法仍然能相对准确的辨识出低频振荡主导模式，验证了其有效性. 关键词： 低频振荡 经验模态分解 改进Prony法 归一化奇异值法

Prony analysis of low frequency oscillations based on empirical mode decomposition filtering

Since traditional Prony analysis of low frequency oscillations has strict requirements to the input signal and is sensitive to the noise of data, this paper proposes a empirical mode decomposition filtering and Prony analysis combined method for low frequency analysis. In this method, empirical mode decomposition is used to adaptively filter the noise of the input signals before improved Prony analysis is carried out. The order of improved Prony analysis is determined by the normalized singular value method. This method is applied to analyze the test signal and the IEEE 4-machine system oscillation signals, and compared with the Prony analysis based on low pass filter. The simulation shows the effectiveness of this method which indicates that the result of the analysis is good even in highly noisy environment.