PowerFlowMultiNet: Multigraph Neural Networks for Unbalanced Three-Phase Distribution Systems
This addresses a critical gap in scalable algorithms for grid analysis and simulation, specifically for unbalanced three-phase power grids, though it is incremental as it extends existing GNN techniques to a new domain.
The paper tackles the problem of solving unbalanced three-phase power flow in distribution grids by introducing PowerFlowMultiNet, a multigraph GNN framework, which achieves significantly lower error rates and a hundredfold increase in computational speed compared to traditional methods.
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 multigraph GNN framework explicitly designed for unbalanced three-phase power grids. The proposed approach models each phase separately in a multigraph representation, effectively capturing the inherent asymmetry in unbalanced grids. A graph embedding mechanism utilizing message passing is introduced to capture spatial dependencies within the power system network. PowerFlowMultiNet outperforms traditional methods and other deep learning approaches in terms of accuracy and computational speed. Rigorous testing reveals significantly lower error rates and a notable hundredfold increase in computational speed for large power networks compared to model-based methods.