Abstract
A multi-sensor multi-temporal data-fusion technique for power swing monitoring is proposed and evaluated. The method combines the ability of statistical techniques to identify the dominant structures in an ensemble of observations with that of time–frequency techniques to extract temporal features. Due to its multi-scale nature, the proposed framework is well suited for the analysis and monitoring of wide-area signals obtained using synchronized phasor measurement units. Features relevant for wide-area inter-area oscillation monitoring are systematically obtained from decentralized data concentrators, and techniques to analyze the distribution of the extracted features are proposed. Methods to quantify the spatial and temporal structure of critical modes are also described, and key research directions in the area of data fusion are discussed. The application of the proposed framework to real data is studied, focusing on the issues of data compression and feature extraction and classification.
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