随着电力系统的快速发展，大规模新能源并网及源网荷储协同优化增加了电力电子设备的比重，使得电网稳定性，特别是暂态稳定性评估，变得尤为重要。针对传统方法在拓扑结构考虑不足的问题，提出了一种基于Transformer-图注意力网络（graph attention network, GAT）并行特征融合的深度学习方法，用于电力系统暂态稳定性评估。以母线电压幅值、相角及拓扑邻接矩阵作为输入特征，利用西门子仿真软件PSS/E生成批量数据，并通过Transformer和GAT并行提取特征，采用注意力机制进行加权融合。与其他方法的对比结果表明，该方法在IEEE 39节点系统中模拟了不同负荷条件和故障工况，结果表明评估精度和鲁棒性较高，能够有效提升电力系统的安全稳定性。

With the rapid development of the power system, the large-scale integration of new energy into the grid and the coordinated optimization of source-grid-load-storage have increased the proportion of power electronic equipment, making the stability of the power grid, especially the assessment of transient stability, particularly important. Aiming at the problem of insufficient consideration of topological structure in traditional methods, a deep learning method based on Transformer-graph attention network(GAT) parallel feature fusion was proposed for the transient stability evaluation of power systems. The busbar voltage amplitude, phase angle and topological adjacentation matrix were taken as input features. Batch data were generated using the Siemens simulation software PSS/E, and features were extracted in parallel through Transformer and GAT. Weighted fusion was carried out using the attention mechanism. The comparison results with other methods show that this method simulates different load conditions and fault conditions in the IEEE 39-node system. The results indicate that the evaluation accuracy and robustness are relatively high, and it can effectively improve the safety and stability of the power system.