电力工程技术2026，Vol.45Issue(4)：123-133,148,12.DOI:10.12158/j.2096-3203.2026.04.013
基于贝叶斯优化图注意力网络的配电网潮流计算方法
Power flow calculation method for distribution network based on Bayesian optimized graph attention networks
摘要
Abstract
A Bayesian optimized graph attention network(BO-GAT)based power flow calculation method is proposed for distribution networks.This method addresses the low computational speed and reliance on complete line parameters of conventional power flow methods.It also overcomes the limitations of existing data-driven approaches in handling frequent topology changes.The method utilizes the topology and node features of the distribution network to construct graph data,and calculates attention coefficients using the graph attention mechanism.By capturing correlations between nodes,the method enhances the adaptability of the power flow regression model to topology changes.The Bayesian optimization(BO)algorithm is introduced to optimize the hyperparameters,further enhancing the performance of the model.The model's regression accuracy and computational efficiency are evaluated on the improved IEEE 33-node system.The results demonstrate that the proposed method can achieve rapid power flow calculation without specific line parameters.It also exhibits strong robustness and topology generalization capability under measurement information loss and topology changes.Moreover,even with a significant increase in wind and solar energy penetration,the calculation accuracy remains high.Finally,the applicability of the proposed method to large-scale distribution networks is further validated on the IEEE 141-node system.关键词
配电网/潮流计算/图注意力网络(GAT)/贝叶斯优化(BO)/拓扑泛化/数据驱动Key words
distribution network/power flow calculation/graph attention network(GAT)/Bayesian optimization(BO)/topological generalization/data-driven分类
信息技术与安全科学引用本文复制引用
季怀招,周云海,赵畅,李欣,罗琰琳,周勇..基于贝叶斯优化图注意力网络的配电网潮流计算方法[J].电力工程技术,2026,45(4):123-133,148,12.基金项目
国家自然科学基金资助项目(52107107) （52107107）
