  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 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 GNNs structures (e.g., graph convolutional networks) are summarized, and key
applications in power systems, such as fault scenario application, time series
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.
