  Deep neural networks have revolutionized many machine learning tasks in power
systems, ranging from pattern recognition to signal processing. The data in
these tasks is 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 the 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
studies on extending 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 GNNs structures (e.g., graph convolutional networks, graph
recurrent neural networks, graph attention networks, graph generative networks,
spatial-temporal graph convolutional networks, and hybrid forms of GNNs) are
summarized, and key applications in power systems such as fault diagnosis,
power prediction, power flow calculation, and data generation are reviewed in
detail. Furthermore, main issues and some research trends about the
applications of GNNs in power systems are discussed.
