电力系统保护与控制2026，Vol.54Issue(6)：58-70,13.DOI:10.19783/j.cnki.pspc.250588
基于改进GCN-Transformer的电力系统脆弱性节点辨识
Power system vulnerability node identification based on improved GCN-Transformer
摘要
Abstract
With the continuous expansion of power systems and the growing penetration of renewable energy,modern power grids have become increasingly complex.Local node failures can easily trigger cascading outages,posing serious threats to system security.Therefore,proactively identifying vulnerable nodes and implementing targeted protection measures is crucial for ensuring safe grid operation.To achieve efficient vulnerable node identification,an improved graph convolutional network-Transformer(GCN-Transformer)method is proposed.First,a set of node vulnerability evaluation indicators is constructed by integrating complex network theory with an improved information entropy-K-shell algorithm.Second,a Chebyshev polynomial-based Kolmogorov-Arnold Network(Cheb-KAN)is introduced as a front-end branch feature extraction module for the graph convolutional network(GCN),enhancing the propagation effectiveness of node feature extraction across different branches.The features extracted by the improved GCN are then fed into a Transformer integrated with a multimodal cross-attention(MCA)mechanism to capture global correlations among different modal features,thereby establishing a deep learning model for vulnerable node identification.Subsequently,multiple operating scenarios are constructed based on the IEEE 39-bus system to build the original dataset for model training.Finally,the proposed model is trained and evaluated on this dataset.Results demonstrate that the proposed method significantly outperforms traditional graph network models in terms of identification accuracy,showing strong feasibility and promising engineering application potential in practical power grid scenarios.关键词
脆弱性节点/脆弱性评价指标/改进GCN-Transformer/Cheb-KAN/多模态交叉注意力/节点辨识Key words
vulnerable node/vulnerability evaluation index/improved GCN-Transformer/Cheb-KAN/multimodal cross-attention/node identification引用本文复制引用
刘伟,梁悦帅..基于改进GCN-Transformer的电力系统脆弱性节点辨识[J].电力系统保护与控制,2026,54(6):58-70,13.基金项目
This work is supported by the National Natural Science Foundation of China(No.62473096). 国家自然科学基金项目资助(62473096) （No.62473096）
