PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systems

arXiv:2403.00892v1 Announce Type: cross
Abstract: Efficiently solving unbalanced three-phase power flow in distribution grids is pivotal for grid analysis and simulation. There is a pressing need for scalable algorithms capable of handling large-scale unbalanced power grids that can provide accurate and fast solutions. To address this, deep learning techniques, especially Graph Neural Networks (GNNs), have emerged. However, existing literature primarily focuses on balanced networks, leaving a critical gap in supporting unbalanced three-phase power grids. This letter introduces PowerFlowMultiNet, a novel …

abstract algorithms analysis arxiv cs.lg cs.sy deep learning deep learning techniques distribution eess.sy flow graph grid networks neural networks pivotal power scalable scale simulation solutions systems type