Estimation of modal parameters from ambient data using singular spectrum analysis

The development of advanced signal processing algorithms to extract modal information from ambient system oscillations has acquired a great deal of interest in recent years. Much of this effort has been directed towards the problem of feature extraction, which involves estimating, identifying and extracting oscillatory phenomena embedded in highly noisy random processes. In this paper, a statistical framework for analyzing ambient signals from power systems is proposed. The method combines singular spectral analysis with a random decrement technique to identify and estimate power system oscillatory phenomena from ambient system oscillations. The paper also describes the experience with the application of this technique to quantify modal information on both synthetic and ambient data.