基于图深度学习的薄弱支路辨识与溯因分析

Identification and Attribution Analysis of Weak Branches Based on Graph Deep Learning

电力系统多样化的运行方式对薄弱支路辨识的速度与拓扑泛化性提出更高要求。结合图深度学习及解释方法对薄弱支路进行辨识与溯因分析。采用基于初始残差和单位映射的图卷积神经网络(graph convolutional network via initial residual and identity mapping,GCNII)搭建薄弱支路辨识模型,模型可基于拓扑关系聚合元件特征,结合邻近电网的安全态势评估支路的薄弱程度。采用基于互信息优化的解释方法分析辨识模型的决策依据,提取薄弱支路的主导因子。IEEE68节点系统、实际电网算例结果表明,辨识模型具有较好的辨识准确性和拓扑泛化性,溯因分析结果符合传统机理认知所得结论,可为连锁故障的实时预警和预防控制提供有效指导。

The diversified mode of power system operation puts forward higher requirements for speed and topological generalization of weak branch identification.In this paper,graph deep learning and interpretation method are used to identify and analyze the weak branches.A weak branch identification model based on graph convolutional network via initial residual and identity mapping(GCNII)is constructed.The model can integrate feature based on topological relation,and evaluate the weakness of branches based on security situation of neighboring power grids.The interpretation method based on mutual information optimization is used to analyze the decision basis of the identification model and extract dominant factors of weak branches.The results of IEEE68 system and actual power grid example show that the identification model has preferable accuracy and topological generalization.The results of attribution analysis are consistent with the conclusions of traditional mechanism cognition,which can provide effective guidance for real-time warning and preventive control of cascading failure.