@inproceedings{f25f8131eda34ea5a5680029b08ed172,
title = "Distributed Power System State Estimation Using Graph Convolutional Neural Networks",
abstract = "State 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.",
author = "Park, {Sang Woo} and Fernando Gama and Javad Lavaei and Somayeh Sojoudi",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE Computer Society. All rights reserved.; 56th Annual Hawaii International Conference on System Sciences, HICSS 2023 ; Conference date: 03-01-2023 Through 06-01-2023",
year = "2023",
language = "English (US)",
series = "Proceedings of the Annual Hawaii International Conference on System Sciences",
publisher = "IEEE Computer Society",
pages = "2756--2765",
editor = "Bui, {Tung X.}",
booktitle = "Proceedings of the 56th Annual Hawaii International Conference on System Sciences, HICSS 2023",
address = "United States",
}