Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks

Abstract

Analyzing the vulnerability of power systems in cascading failures is generally regarded as a challenging problem. Although existing studies can extract some critical rules, they fail to capture the complex subtleties under different operational conditions. In recent years, several deep learning methods have been applied to address this issue. However, most of the existing deep learning methods consider only the grid topology of a power system in terms of topological connections, but do not encompass a power system’s spatial information such as the electrical distance to increase the accuracy in the process of graph convolution. In this paper, we construct a novel power-weighted line graph that uses power system topology and spatial information to optimize the edge weight assignment of the line graph. Then we propose a multi-graph convolutional network (MGCN) based on a graph classification task, which preserves a power system’s spatial correlations and captures the relationships among physical components. Our model can better handle the problem with power systems that have parallel lines, where our method can maintain desirable accuracy in modeling systems with these extra topology features. To increase the interpretability of the model, we present the MGCN using layer-wise relevance propagation and quantify the contributing factors of model classification.

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。