Research Area:  Machine Learning
Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks are typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high-dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNN structures, e. g., graph convolutional networks, are summarized. Key applications in power systems such as fault scenario application, time-series prediction, power flow calculation, and data generation are reviewed in detail. Further-more, main issues and some research trends about the applications of GNNs in power systems are discussed.
Graph Neural Networks
Author(s) Name:  Wenlong Liao; Birgitte Bak-Jensen; Jayakrishnan Radhakrishna Pillai; Yuelong Wang; Yusen Wang
Journal name:  Journal of Modern Power Systems and Clean Energy
Publisher name:  IEEE
Volume Information:  Volume: 10, Issue: 2, March 2022,Page(s): 345 - 360
Paper Link:   https://ieeexplore.ieee.org/abstract/document/9520300