基 于 多 尺 度 图 注 意 力 网 络 的 电 力 系 统 暂 态 稳 定 评 估

Existing transient stability assessment methods based on graph deep learning consider the topological structure characteristics of power grids. However, the information transmission characteristics among multi-scale subgraphs in the topological structure of power grids are not effectively modeled, resulting in the insufficient capturing of the local and global dynamic coupling relationship of power grids by the stability judgment model, which reduces the stability judgment accuracy of the model under complex perturbations. Therefore, an assessment method for power angle transient stability integrating the information transmission process of multi-scale subgraphs is proposed. Firstly, a k-dimensional graph attention network is proposed and constructed, which regards the different-scale power grid topology subgraphs as the basic unit for feature extraction in graph deep learning. Then, adaptive weights are assigned to the feature aggregation through the attention mechanism to mine the characteristics between different fine-grained regions in the actual power grid. Finally, the feasibility and effectiveness of the proposed method are verified through the CEPRI-TAS-173 system.