Unified Heterogeneous Graph Neural Network solver for Power Flow, Optimal Power Flow and State Estimation

2026-09-15

AI Summary

该研究提出了一种统一的异构图神经网络模型，通过共享骨干解决电力系统中的潮流计算、最优潮流和状态估计问题，提高了模型的泛化能力和效率。

Abstract

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.

Problem

• Power Flow
• Optimal Power Flow
• State Estimation
• Graph Neural Networks
• Heterogeneous Graph

Innovation

• Heterogeneous Graph Neural Network
• Power System Analysis
• Unified Solver