Location
Online
Event Website
https://hicss.hawaii.edu/
Start Date
3-1-2023 12:00 AM
End Date
7-1-2023 12:00 AM
Description
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.
Recommended Citation
Park, Sangwoo; Gama, Fernando; Lavaei, Javad; and Sojoudi, Somayeh, "Distributed Power System State Estimation Using Graph Convolutional Neural Networks" (2023). Hawaii International Conference on System Sciences 2023 (HICSS-56). 2.
https://aisel.aisnet.org/hicss-56/es/resilient_networks/2
Distributed Power System State Estimation Using Graph Convolutional Neural Networks
Online
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.
https://aisel.aisnet.org/hicss-56/es/resilient_networks/2