PowerFlowNet: Power Flow Approximation Using Message Passing Graph Neural Networks

Accurate and efficient power flow (PF) analysis is crucial in modern electrical networks' operation and planning. Therefore, there is a need for scalable algorithms that can provide accurate and fast solutions for both small and large scale power networks. As the power network can be interpreted as a graph, Graph Neural Networks (GNNs) have emerged as a promising approach for improving the accuracy and speed of PF approximations by exploiting information sharing via the underlying graph structure. In this study, …

algorithms analysis approximation cs.ai cs.lg cs.sy eess.sy flow graph graph neural networks interpreted modern network networks neural networks planning power scalable scale small solutions