Online Event Detection in Synchrophasor Data with Graph Signal Processing

Conference · Wed Nov 11 04:00:00 EST 2020 · 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)

DOI:https://doi.org/10.1109/smartgridcomm47815.2020.9302947· OSTI ID:1867799

Shi, Jie ^[1]; Foggo, Brandon; Kong, Xianghao; Cheng, Yuanbin; Yu, Nanpeng; Yamashita, Koji

---

1. University of California, Riverside

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.

• Research Organization: University of California, Riverside
• Sponsoring Organization: USDOE Office of Electricity (OE)
• DOE Contract Number: OE0000916
• OSTI ID: 1867799
• Report Number(s): Conference paper: DOE-UCR-2020-SmartGridComm
• Conference Information: Journal Name: 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
• Country of Publication: United States
• Language: English

Related Subjects

Event detection
graph Fourier transform
graph signal processing
phasor measurement unit