Visible to the public Online Event Detection in Synchrophasor Data with Graph Signal Processing

TitleOnline Event Detection in Synchrophasor Data with Graph Signal Processing
Publication TypeConference Paper
Year of Publication2020
AuthorsShi, Jie, Foggo, Brandon, Kong, Xianghao, Cheng, Yuanbin, Yu, Nanpeng, Yamashita, Koji
Conference Name2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
Date Publishednov
KeywordsComputers, Conferences, control theory, event detection, graph Fourier transform, graph signal processing, Human Behavior, Laplace equations, Phasor Measurement Unit, phasor measurement units, pubcrawl, resilience, Resiliency, Scalability, Smart grids, Training data
AbstractOnline 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.
Citation Keyshi_online_2020