CLC number: TP393
On-line Access: 2022-12-14
Received: 2022-01-27
Revision Accepted: 2022-12-17
Crosschecked: 2022-03-24
Cited: 0
Clicked: 1551
Citations: Bibtex RefMan EndNote GB/T7714
https://orcid.org/0000-0002-4032-7740
Supaporn LONAPALAWONG, Changsheng CHEN, Can WANG, Wei CHEN. Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200035 @article{title="Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks", %0 Journal Article TY - JOUR
连锁故障中电力系统脆弱性的多图卷积网络分析1浙江大学计算机辅助设计与图形学国家重点实验室,中国杭州市,310058 2中国电力科学研究院,中国北京市,100192 3浙江大学计算机科学与技术学院,中国杭州市,310058 摘要:分析电力系统在连锁故障中的薄弱环节是电力系统分析领域极具挑战的难题。电力系统领域的传统分析方法虽能发现一些简单的传播规律,但却难以捕捉不同运行条件下的复杂细节。近年来的研究引入了深度学习算法来解决这一难题。然而,现有基于深度学习的方法大多仅从拓扑层面考虑电力系统的网架结构,未能充分考虑空间信息(如电距离)以提高图卷积过程的精确度。鉴于此,本文提出一种新型电力系统加权线图,综合考虑电力系统拓扑结构和空间信息,大幅优化线图的边权分配。此外,本文提出一种基于图分类任务的多图卷积网络(MGCN),在保留电力系统空间相关性的同时有效捕获物理元件之间的关联。经验证,该模型能够在具有额外拓扑特征的建模系统中保持理想精度,从而更好地分析存在并行输电线路的复杂连锁故障。最后,本文采用逐层相关传播方法解释MGCN,并量化了模型分类的贡献因子,有效提升模型的可解释性。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
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