Distributed Power System State Estimation Using Graph Convolutional Neural Networks

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dc.contributor.authorPark, Sangwoo
dc.contributor.authorGama, Fernando
dc.contributor.authorLavaei, Javad
dc.contributor.authorSojoudi, Somayeh
dc.date.accessioned2022-12-27T19:05:21Z
dc.date.available2022-12-27T19:05:21Z
dc.date.issued2023-01-03
dc.description.abstractState estimation plays a key role in guaranteeing the safe and reliable operation of power systems. This is a complex problem due to the noisy and unreliable nature of the measurements that are obtained from the power grid. Furthermore, the laws of physics introduce nonconvexity, which makes the use of efficient optimization-based techniques more challenging. In this paper, we propose to use graph convolutional neural networks (GCNNs) to learn state estimators from data. The resulting estimators are distributed and computationally efficient, making them robust to cyber-attacks on the grid and capable of scaling to large networks. We showcase the promise of GCNNs in distributed state estimation of power systems in numerical experiments on IEEE test cases.
dc.format.extent10
dc.identifier.doi10.24251/HICSS.2023.339
dc.identifier.isbn978-0-9981331-6-4
dc.identifier.urihttps://hdl.handle.net/10125/102970
dc.language.isoeng
dc.relation.ispartofProceedings of the 56th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectResilient Networks
dc.subjectconvolutional neural networks
dc.subjectgraph neural networks
dc.subjectpower system monitoring
dc.subjectstate estimation
dc.titleDistributed Power System State Estimation Using Graph Convolutional Neural Networks
dc.type.dcmitext
prism.startingpage2756

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