电力系统自动化2025，Vol.49Issue(3)：60-70,11.DOI:10.7500/AEPS20240318003
基于多尺度图注意力网络的电力系统暂态稳定评估
Transient Stability Assessment of Power Systems Based on Multi-scale Graph Attention Network
摘要
Abstract
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.关键词
暂态稳定评估/深度学习/多尺度子图/特征提取/图注意力网络Key words
transient stability assessment/deep learning/multi-scale subgraph/feature extraction/graph attention network引用本文复制引用
傅太国屹,杜友田,吕昊,李宗翰,刘俊..基于多尺度图注意力网络的电力系统暂态稳定评估[J].电力系统自动化,2025,49(3):60-70,11.基金项目
国家重点研发计划资助项目(2021YFB2400800). This work is supported by National Key R&D Program of China(No.2021YFB2400800). （2021YFB2400800）
