Frontiers of Information Technology & Electronic Engineering
>> 2022,
Volume 23,
Issue 12
doi:
10.1631/FITEE.2200035
Interpreting the vulnerability of power systems in cascading failures using multi-graph convolutional networks
Affiliation(s): State Key Lab of CAD & CG, Zhejiang University, Hangzhou 310058, China; China Electric Power Research Institute, Beijing 100192, China; College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China; less
Received: 2022-01-27
Accepted: 2022-12-14
Available online: 2022-12-14
Next
Previous
Abstract
Analyzing the of in 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- 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 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.
京公网安备 11010502051620号
