用于输电网拓扑控制的图神经网络：母线信息不对称与异质表示
Graph Neural Networks for Transmission Grid Topology Control: Busbar Information Asymmetry and Heterogeneous Representations
AI总结：
针对输电网拓扑控制中同质图表示存在的母线信息不对称问题，提出异质图表示方法，实验证明异质GNN在分布内配置下表现最优，且两类GNN泛化能力均优于FCNN基线。
摘要：
可再生能源普及与电气化等因素导致电网拥堵成为一个亟待解决的问题。拓扑控制是缓解拥堵的一种颇具吸引力的方法，但传统的拓扑发现方法速度过慢，无法满足实际应用需求。近期研究聚焦于将机器学习（ML）作为一种高效替代方案。图神经网络（GNN）因具备建模电网图结构的能力，尤其适用于拓扑控制场景。本研究探究了图表示对GNN在拓扑控制任务中有效性的影响。我们指出了当前主流的同质图表示固有的母线信息不对称问题，并提出了一种可解决该问题的异质图表示方法。我们在一项模仿学习任务中，分别采用搭载两种图表示的GNN以及全连接神经网络（FCNN）基线模型进行实验，通过分类准确率和电网运行能力评估模型性能。结果显示，异质GNN在分布内网络配置下表现最优，其次是FCNN，同质GNN表现最差。此外我们还发现，两类GNN对分布外网络配置的泛化能力均优于FCNN。
英文摘要：
Factors such as the proliferation of renewable energy and electrification contribute to grid congestion as a pressing problem. Topology control is an appealing method for relieving congestion, but traditional approaches for topology discovery have proven too slow for practical application. Recent research has focused on machine learning (ML) as an efficient alternative. Graph neural networks (GNNs) are particularly well-suited for topology control applications due to their ability to model the graph structure of power grids. This study investigates the effect of the graph representation on GNN effectiveness for topology control. We identify the busbar information asymmetry problem inherent to the popular homogeneous graph representation. We propose a heterogeneous graph representation that resolves this problem. We apply GNNs with both representations and a fully connected neural network (FCNN) baseline on an imitation learning task. The models are evaluated by classification accuracy and grid operation ability. We find that heterogeneous GNNs perform best on in-distribution network configurations, followed by FCNNs, and lastly, homogeneous GNNs. We also find that both GNN types generalize better to out-of-distribution network configurations than FCNNs.
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