AI 中文总结
本文提出一种统一异构残差门控图卷积网络，通过共享主干同时求解潮流、最优潮流和状态估计，在IEEE 14和118节点系统上达到与专用求解器相当的精度，并具备鲁棒性，为电力系统基础模型奠定基础。
AI 中文摘要
潮流计算（PF）、最优潮流（OPF）和状态估计（SE）是电力系统分析中的基本问题，但求解这些问题的计算成本很高。图神经网络（GNNs）已被提出作为快速替代模型，然而现有的求解器每次仅针对单一问题进行训练，产生的模型较为狭窄，且必须为每个新任务重新构建。我们提出了一种更通用的方法：一个单一的异构残差门控图卷积网络，通过一个共享主干网络同时解决这三个问题。该模型不是学习单一的映射，而是学习网络行为方式的可复用表示，从中可以分别估计PF、OPF和SE。该模型在多种拓扑和负载条件下对这三个问题联合训练，并在IEEE 14节点和118节点系统上进行评估，共享模型在精度上与任务特定的GNN求解器相匹配，并且在未见过的负载水平和拓扑上保持鲁棒性。这些结果表明，单一模型可以捕获电力网络的基本运行特性，并同时服务于多个分析任务，这是迈向电力系统基础模型的第一步。
英文摘要
Power Flow (PF), Optimal Power Flow (OPF), and State Estimation (SE) are fundamental problems in power system analysis, but solving them is computationally expensive. Graph Neural Networks (GNNs) have been proposed as fast surrogates, yet existing solvers are trained for a single problem at a time, producing narrow models that must be rebuilt for each new task. We propose a more general approach: a single Heterogeneous Residual Gated Graph Convolutional Network that solves all three problems with one shared backbone. Rather than learning one mapping, the model learns a reusable representation of how the network behaves, from which PF, OPF, and SE can each be estimated. Trained jointly on the three problems across diverse topologies and loading conditions, and evaluated on the IEEE 14-bus and 118-bus systems, the shared model matches the accuracy of task-specific GNN solvers and stays robust on unseen loading levels and topologies. These results show that a single model can capture the basic operation of a power network and serve several analysis tasks at once, a first step toward a foundation model for power systems.
