Title

Online event detection in synchrophasor data with graph signal processing

Document Type

Conference Proceeding

Publication Date

12-30-2020

Department

Department of Electrical and Computer Engineering

Abstract

Online detection of anomalies is crucial to enhancing the reliability and resiliency of power systems. We propose a novel data-driven online event detection algorithm with synchrophasor data using graph signal processing. In addition to being extremely scalable, our proposed algorithm can accurately capture and leverage the spatio-temporal correlations of the streaming PMU data. This paper also develops a general technique to decouple spatial and temporal correlations in multiple time series. Finally, we develop a unique framework to construct a weighted adjacency matrix and graph Laplacian for product graph. Case studies with real-world, large-scale synchrophasor data demonstrate the scalability and accuracy of our proposed event detection algorithm. Compared to the state-of-the-art benchmark, the proposed method not only achieves higher detection accuracy but also yields higher computational efficiency.

Publisher's Statement

© 2020 IEEE. Publisher’s version of record: https://doi.org/10.1109/SmartGridComm47815.2020.9302947

Publication Title

2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2020

ISBN

9781728161273

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