深度神经网络彻底改变了电力系统中许多机器学习任务，从模式识别到信号处理。这些任务中的数据通常以欧几里得域表示。然而，在电力系统中有越来越多的应用程序，其中从非欧氏域中收集数据并表示为具有高维特征和节点间相互依赖关系的图结构数据。图结构化数据的复杂性给在欧几里得域中定义的现有深度神经网络带来了重大挑战。最近，出现了许多关于在电力系统中扩展深度神经网络以用于图结构数据的研究。本文提出了电力系统中图神经网络（GNN）的全面概述。特别，总结了GNN结构的几种经典范例（例如，图卷积网络，图递归神经网络，图注意力网络，图生成网络，时空图卷积网络以及GNN的混合形式），并在电力系统中的关键应用如详细审查了故障诊断，功率预测，潮流计算和数据生成。此外，还讨论了有关GNN在电力系统中的应用的主要问题和一些研究趋势。和数据生成进行了详细的审查。此外，还讨论了有关GNN在电力系统中的应用的主要问题和一些研究趋势。和数据生成进行了详细的审查。此外，还讨论了有关GNN在电力系统中的应用的主要问题和一些研究趋势。 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.