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.

PowerFlowMultiNet 是一种为不平衡三相电力网络明确设计的新型多图神经网络框架，它以多图表示方式模拟每个相位，有效捕捉了不平衡电网中的固有不对称性，并引入了一种利用消息传递进行图嵌入的机制来捕捉电力系统网络内的空间依赖关系。与传统方法和其他深度学习方法相比，PowerFlowMultiNet 在精确度和计算速度方面表现出色，在大型电力网络方面的误差率显著降低，并且计算速度提高了数百倍。

PowerFlowMultiNet：用于非平衡三相配电系统的多图神经网络

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

In this paper, we propose a new model that is capable of recognizing
overlapping mentions. We introduce a novel notion of mention separators that
can be effectively used to capture how mentions overlap with one another. On
top of a novel multigraph representation that we introduce, we show that
efficient and exact inference can still be performed. We present some
theoretical analysis on the differences between our model and a recently
proposed model for recognizing overlapping mentions, and discuss the possible
implications of the differences. Through extensive empirical analysis on
standard datasets, we demonstrate the effectiveness of our approach.

该论文提出了一种新模型，能够识别重叠提及，引入了一个新的提及分隔符概念和多图表示，能够有效地捕捉提及之间重叠的情况，并证明可以实现高效准确的推理，此外，通过在标准数据集上进行大量的实证分析，证明了该方法的有效性。