A Review of Graph Neural Networks and Their Applications in Power Systems

TL;DR

This paper provides a comprehensive review of graph neural networks (GNNs) and their diverse applications in power systems, highlighting recent developments, challenges, and future research directions in this emerging field.

Contribution

It offers a detailed overview of GNN structures and their applications in power systems, addressing current challenges and identifying future research trends.

Findings

GNNs effectively model non-Euclidean data in power systems.

Applications include fault detection, time series prediction, and power flow analysis.

The paper discusses key issues and future research directions in GNN applications.

Abstract

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.