基于图注意力网络和一致性风险控制的配电网故障区段定位方法

A Fault Section Location Method Based on Graph Attention Network and Conformal Risk Control in Distribution Network

配电网运行方式灵活、拓扑结构变化频繁,现有基于人工智能算法的配电网故障区段定位方法拓扑泛化性差。该文基于图注意力网络(graph attention network,GAT)构建了配电网故障区段定位底层模型,结合配电网拓扑结构充分挖掘配电网故障特征,以提高模型的拓扑泛化能力。此外,引入了一致性风险控制(conformal risk control,CRC)方法,构建了具备可靠性的配电网故障区段定位模型,使模型的预测风险人为可控。依托IEEE-33节点系统的算例结果表明,基于GAT和CRC的故障区段定位模型具有定位准确率高、鲁棒性强和拓扑泛化性好的优点,在双重故障和高阻故障下均有良好的表现,而且模型对于线路参数变化也具有一定的泛化能力。

The distribution network operates flexibly and the topology changes frequently.The existing fault section location method of the distribution network based on artificial intelligence algorithm has poor topological generalization.In this paper,the underlying model of fault section location in distribution network is proposed based on graph attention network(GAT).This model fully explores the fault characteristics based on the topology of the distribution network to improve its topological generalization.In addition,the conformal risk control(CRC)method is introduced to construct a reliable model of fault section location in distribution network,so that the prediction risk of the model is artificially controllable.The results of IEEE-33 system show that the fault section location model based on GAT and CRC has preferable accuracy,robustness and topological generalization.Moreover,the model has good performance under double fault and high resistance fault,and has certain generalization ability for the change of distribution network line parameters.