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Online Event Detection in Synchrophasor Data with Graph Signal Processing
- Source :
- SmartGridComm
- Publication Year :
- 2020
- Publisher :
- IEEE, 2020.
-
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.
- Subjects :
- Computer science
Reliability (computer networking)
05 social sciences
050801 communication & media studies
computer.software_genre
Graph
0508 media and communications
0502 economics and business
Benchmark (computing)
Graph (abstract data type)
Leverage (statistics)
050211 marketing
Data mining
Adjacency matrix
Laplacian matrix
computer
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
- Accession number :
- edsair.doi...........4810bf1055e73fea825e1e151bc7e509