[论文解读] A Review of Graph Neural Networks and Their Applications in Power Systems

Wenlong Liao, Birgitte Bak‐Jensen+3 more|arXiv (Cornell University)|Jan 25, 2021

Advanced Graph Neural Networks参考文献 103被引用 26

一句话总结

本文综述图神经网络（GNNs）及其在电力系统中的应用，概述 GNN 范式及关键任务，如故障分析、时间序列预测、潮流计算和数据生成。

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.

研究动机与目标

• 推动在电力系统中处理非欧几里得、图结构数据。
• 提供经典 GNN 范式（例如 graph convolutional networks） 的全面概述及其在电力系统中的适用性。
• 回顾 GNN 在电力系统中的关键应用，包括故障分析、时间序列预测、潮流计算和数据生成。
• 讨论将 GNN 应用于电力系统的主要问题和新兴研究趋势。

提出的方法

• 总结经典 GNN 结构及其对图结构数据的适应。
• 对电力系统中基于 GNN 的应用进行分类与评述（故障情景、时间序列、潮流计算、数据生成）。
• 突出在电力系统中 GNN 的挑战、局限性以及潜在研究方向。

实验结果

研究问题

• RQ1 哪些图神经网络范式与电力系统最相关？
• RQ2 GNNs 如何应用于电力系统的故障情景分析、时间序列预测、潮流计算和数据生成？
• RQ3 在将 GNNs 应用于电力系统时，主要问题和研究趋势是什么？

主要发现

• 本文全面概述了 GNNs 及其结构，重点关注 graph convolutional networks。
• 它回顾了电力系统中的关键应用，包括故障情景分析、时间序列预测、潮流计算和数据生成。
• 它讨论了与电力系统中的 GNNs 相关的主要问题和研究趋势。