用图注意力网络重建电力系统潮流模型，提升动态网络下的建模精度与泛化能力。
Rebuild AC Power Flow Models with Graph Attention Networks
- 基于节点电压实虚部构建新图结构，用GAT重构潮流模型。
- 在不同规模和拓扑的IEEE测试系统上，误差低于现有方法。
- 适合电网拓扑动态变化场景，适用于电力系统实时分析与预测。
完整的潮流（PF）模型是电力网络物理特性的完整表征。传统基于模型的方法依赖完整PF模型进行潮流分析。然而，在实际中，由于电力系统的不确定性或动态变化，部分模型参数可能不准确甚至缺失。此外，随着电网持续演进且拓扑可能改变，潮流模型对不同网络规模和类型应具备良好泛化能力。本文提出一种基于图注意力网络（GAT）的潮流重建模型，通过构建基于各母线电压实部与虚部的新图结构实现建模。在多个标准IEEE电力系统案例及其拓扑变体上，与两种前沿潮流重建模型对比，验证了本方法的可行性。实验结果表明，所提模型在网络变化条件下具有更优精度，并能有效推广至不同网络，精度下降较小。
原文摘要 · Abstract (English)
A full power flow (PF) model is a complete representation of the physical power network. Traditional model-based methods rely on the full PF model to implement power flow analysis. In practice, however, some PF model parameters can be inaccurate or even unavailable due to the uncertainties or dynamics in the power systems. Moreover, because the power network keeps evolving with possibly changing topology, the generalizability of a PF model to different network sizes and typologies should be considered. In this paper, we propose a PF rebuild model based on graph attention networks (GAT) by constructing a new graph based on the real and imaginary parts of voltage at each bus. By comparing with two state-of-the-art PF rebuild models for different standard IEEE power system cases and their modified topology variants, we demonstrate the feasibility of our method. Experimental results show that our proposed model achieves better accuracy for a changing network and can generalize to different networks with less accuracy discount.
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