Extracting Low-Frequency Spatio-Temporal Patterns in Ambient Power System Data Using Blind Source Separation

AbstractWide-area techniques provide a powerful tool to extract spatio-temporal patterns from high-dimensional datasets and can be used for event detection and visualization, data fusion, stability assessment, and coherency analysis. In this paper, a novel blind source separation-based approach for extracting low-frequency spatio-temporal patterns from measured ambient power system data is proposed and a spatio-temporal visualization index is also suggested. This methodology combines a nonlinear hierarchical neural network with a Blind Source Separation (BSS) technique. The neural network allows reducing noise and removing the nonlinear relations among data (preserve dynamic features of interest), while the BSS technique permits extracting spatial and temporal patterns. In addition, the proposed approach takes advantage of the latest techniques in nonlinear estimation of non-stationary time series. Finally, application examples of the proposed framework on real test cases recorded from an actual power sys...