Learning Latent Interactions for Event Identification via Graph Neural Networks and PMU Data

Yuan, Yuxuan; Wang, Zhaoyu; Wang, Yanchao

doi:10.1109/tpwrs.2022.3158248

Learning Latent Interactions for Event Identification via Graph Neural Networks and PMU Data

Journal Article · Thu Mar 10 00:00:00 EST 2022 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2022.3158248· OSTI ID:1866732

Yuan, Yuxuan ^[1]; Wang, Zhaoyu ^[2]; Wang, Yanchao ^[2]

Iowa State Univ., Ames, IA (United States); Iowa State University
Iowa State Univ., Ames, IA (United States)

Phasor measurement units (PMUs) are being widely installed on power systems, providing a unique opportunity to enhance wide-area situational awareness. One essential application is the use of PMU data for real-time event identification. However, how to take full advantage of all PMU data in event identification is still an open problem. Thus, we propose a novel method that performs event identification by mining interaction graphs among different PMUs. The proposed interaction graph inference method follows an entirely data-driven manner without knowing the physical topology. Moreover, unlike previous works that treat interactive learning and event identification as two different stages, our method learns interactions jointly with the identification task, thereby improving the accuracy of graph learning and ensuring seamless integration between the two stages. Moreover, to capture multi-scale event patterns, a dilated inception-based method is investigated to perform feature extraction of PMU data. To test the proposed data-driven approach, a large real-world dataset from tens of PMU sources and the corresponding event logs have been utilized in this work. We report numerical results validate that our method has higher classification accuracy compared to previous methods.

View Accepted Manuscript (DOE)

Research Organization:: Iowa State University, Ames, IA (United States)

Sponsoring Organization:: USDOE Office of Electricity (OE)

Grant/Contract Number:: OE0000910

OSTI ID:: 1866732

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 1 Vol. 38; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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