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Shi, Jie, Foggo, Brandon, Kong, Xianghao, Cheng, Yuanbin, Yu, Nanpeng, Yamashita, Koji.  2020.  Online Event Detection in Synchrophasor Data with Graph Signal Processing. 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm). :1—7.
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.