Power Flow Approximation Based on Graph Convolutional Networks

In this article we develop a graph convolutional neural network (GCN) for the approximation of the AC power flow in electrical power grids. The proposed architecture is fully generic and purely data driven, such that no information about the actual underlying physical topology of the power grid is required. This gives the opportunity to apply this approach to a wide range of multivariate regression problems. We test our architecture on 3 datasets of different sizes, two of which are real world type power grids containing up to 5488 nodes. We show that the proposed method allows an accurate approximation of the power flow specifically in the case of large power grids. The GCN architecture implies intrinsic extrapolation, allowing a reasonable reduction of the number of trainable parameters as well as training samples. In contrast, classical approaches based on fully connected networks are shown to face difficulties when fitting such high dimensional functions.