计算机科学
卷积神经网络
图形
估计
国家(计算机科学)
人工智能
分布式计算
理论计算机科学
算法
工程类
系统工程
作者
SangWoo Park,Fernando Gama,Javad Lavaei,Somayeh Sojoudi
出处
期刊:Proceedings of the ... Annual Hawaii International Conference on System Sciences
日期:2023-01-01
被引量:1
标识
DOI:10.24251/hicss.2023.339
摘要
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.
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