| 引用本文: | 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(06):58-70. |
| LIU Wei,LIANG Yueshuai.Power system vulnerability node identification based on improved GCN-Transformer[J].Power System Protection and Control,2026,54(06):58-70 |
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| 摘要: |
| 随着电力系统规模不断扩大和新能源接入比例增加，电网结构日趋复杂。局部节点故障易引发电网连锁失效，对系统安全构成严重威胁。因此，预先识别电网中的脆弱性节点并加以保护对保障电网的安全运行至关重要。为实现脆弱性节点的高效辨识，提出一种改进融合图卷积网络与Transformer架构(graph convolutional network- Transformer, GCN-Transformer)的脆弱性节点辨识方法。首先，结合复杂网络理论和改进信息熵-K壳算法构建节点脆弱性评价指标集。其次，引入基于Chebyshev多项式的Kolmogorov-Arnold网络(Chebyshev Kolmogorov-Arnold network, Cheb-KAN)作为图卷积网络(graph convolutional networks, GCN)的前置支路特征提取模块，优化GCN的节点特征提取在不同支路间的传播效果。同时，将改进GCN提取的特征输入至融合了多模态交叉注意力机制(multimodal cross-attention mechanism, MCA)的Transformer中，用以捕获不同模态特征间的全局关联关系，构建面向脆弱节点辨识的深度学习模型。然后，基于IEEE39节点构建多种工况运行场景，建立模型训练的原始数据集。最后，在原始数据集上对所提模型进行训练与评估。结果表明，该方法在脆弱节点辨识准确率方面显著优于传统图网络模型，具备良好的可行性及其在电网实际场景中的工程应用潜力。 |
| 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 |
| DOI：10.19783/j.cnki.pspc.250588 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(62473096) |
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| Power system vulnerability node identification based on improved GCN-Transformer |
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LIU Wei1,2，LIANG Yueshuai2
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1. NEPU Sanya Offshore Oil&Gas Research Institute, Sanya 572000, China; 2. School of Electrical and
Information Engineering, Northeast Petroleum University, Daqing 163000, China
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| 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. |
| Key words: vulnerable node vulnerability evaluation index improved GCN-Transformer Cheb-KAN multimodal cross-attention node identification |
