"Online event detection in synchrophasor data with graph signal process" by Jie Shi, Brandon Foggo et al.
 

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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