| Title | Online Event Detection in Synchrophasor Data with Graph Signal Processing |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Shi, Jie, Foggo, Brandon, Kong, Xianghao, Cheng, Yuanbin, Yu, Nanpeng, Yamashita, Koji |
| Conference Name | 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm) |
| Date Published | nov |
| Keywords | Computers, 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 |
| 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. |
| DOI | 10.1109/SmartGridComm47815.2020.9302947 |
| Citation Key | shi_online_2020 |
Online Event Detection in Synchrophasor Data with Graph Signal Processing