Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification

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dc.contributor.authorZheng, Xiangtian
dc.contributor.authorWang, Bin
dc.contributor.authorKalathil, Dileep
dc.contributor.authorXie, Le
dc.creator.orcidhttps://orcid.org/0000-0003-4199-4403en_US
dc.date.accessioned2023-04-27T17:07:41Z
dc.date.available2023-04-27T17:07:41Z
dc.date.issued2021-02-24
dc.description.abstractA two-stage machine learning-based approach for creating synthetic phasor measurement unit (PMU) data is proposed in this article. This approach leverages generative adversarial networks (GAN) in data generation and incorporates neural ordinary differential equation (Neural ODE) to guarantee underlying physical meaning. We utilize this approach to synthetically create massive eventful PMU data, which would otherwise be difficult to obtain from the real world due to the critical energy infrastructure information (CEII) protection. To illustrate the utility of such synthetic data for subsequent data-driven methods, we specifically demonstrate the application of using synthetic PMU data for event classification by scaling up the real data set. The addition of the synthetic PMU data to a small set of real PMU data is shown to have improved the event classification accuracy by 2 to 5 percent.en_US
dc.description.departmentElectrical and Computer Engineeringen_US
dc.identifier.citationZheng, X., Wang, B., Kalathil, D., & Xie, L. (2021). Generative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classification. IEEE Open Access Journal of Power and Energy, 8, 68-76. doi:10.1109/OAJPE.2021.3061648en_US
dc.identifier.issn2687-7910
dc.identifier.otherhttps://doi.org/10.1109/OAJPE.2021.3061648
dc.identifier.urihttps://hdl.handle.net/20.500.12588/1822
dc.language.isoen_USen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.rightsAttribution 3.0 United States*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/us/*
dc.subjectphasor measurement unitsen_US
dc.subjectpower system dynamicsen_US
dc.subjectmathematical modelen_US
dc.subjectgallium nitrideen_US
dc.subjectgeneratorsen_US
dc.subjectdata modelsen_US
dc.subjectcomputational modelingen_US
dc.subjectevent classificationen_US
dc.subjectgenerative adversarial networken_US
dc.subjectneural ODEen_US
dc.titleGenerative Adversarial Networks-Based Synthetic PMU Data Creation for Improved Event Classificationen_US
dc.typeArticleen_US

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