Online Event Detection in Synchrophasor Data with Graph Signal Processing

Conference · Mon Oct 05 04:00:00 EDT 2020

OSTI ID:1670206

Shi, Jie ^[1]; Foggo, Brandon ^[2]; Kong, Xianghao ^[2]; Cheng, Yuanbin ^[2]; Yu, Nanpeng ^[2]; Yamashita, Koji ^[3]

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1. University of California, Riverside; University of California, Riverside
2. University of California, Riverside
3. Michigan Technological Univ., Houghton, MI (United States)

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: 1670206
• Report Number(s): DOE-UCR-0000916
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
Event detection
graph Fourier transform
graph signal processing
phasor measurement unit