Power System Event Identification Based on Deep Neural Network With Information Loading

Shi, Jie; Foggo, Brandon; Yu, Nanpeng

doi:10.1109/tpwrs.2021.3080279

Power System Event Identification Based on Deep Neural Network With Information Loading

Journal Article · Fri May 14 00:00:00 EDT 2021 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2021.3080279· OSTI ID:1867795

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Univ. of California, Riverside, CA (United States); University of California, Riverside
Univ. of California, Riverside, CA (United States)

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this study, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.

View Accepted Manuscript (DOE)

Research Organization:: Univ. of California, Riverside, CA (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: OE0000916

OSTI ID:: 1867795

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 6 Vol. 36; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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