Wide-area PMU data monitoring using spatio-temporal statistical models

In this paper, a statistically-based, data-driven framework that integrates the use of empirical orthogonal function (EOF) analysis and a time-frequency method is proposed to identify and extract, relevant dynamically independent spatio-temporal patterns from time synchronized data. Using time-frequency methods, the temporal signals at selected system locations are decomposed into modal approximations at different scales. Multi-scale EOF analysis is then used to extract cross-correlations across the measurement sites. The method allows for the extremely nonstationary behavior of interarea oscillations to be analyzed into separate frequency bands and is capable of detecting propagating features in non-stationary processes.