用图神经网络实现低压配电网电压精准估计，提升系统可观测性。
Learning a Generalized Model for Substation Level Voltage Estimation in Distribution Networks
- 构建分层图神经网络，融合电网拓扑与物理特征进行电压预测。
- 在仅1%测量覆盖率下仍保持高精度，误差比现有方法低2倍。
- 适用于高比例分布式能源接入的复杂配电网，适合电力系统工程师使用。
配电网中精确的电压估计对实时监控和电网可靠性至关重要。随着分布式能源渗透率上升及配电电压波动加剧，鲁棒的配电系统状态估计（DSSE）对于保障安全高效运行愈发关键。传统方法在测量稀疏和现代馈线规模下表现受限，难以扩展至大规模网络。本文提出一种用于变电站层级电压估计的分层图神经网络，利用电气拓扑与物理特征，在真实配电网常见的低可观测条件下仍具鲁棒性。基于公开的SMART-DS数据集，模型在多个变电站及不同分布式能源渗透率场景下训练与评估，涵盖数千个母线。实验表明，所提方法相比其他数据驱动模型，均方根误差（RMSE）降低最多达2倍，且在仅1%测量覆盖率时仍保持高精度。结果表明，GNN有望实现可扩展、可复现、数据驱动的配电网电压监测。
原文摘要 · Abstract (English)
Accurate voltage estimation in distribution networks is critical for real-time monitoring and increasing the reliability of the grid. As DER penetration and distribution level voltage variability increase, robust distribution system state estimation (DSSE) has become more essential to maintain safe and efficient operations. Traditional DSSE techniques, however, struggle with sparse measurements and the scale of modern feeders, limiting their scalability to large networks. This paper presents a hierarchical graph neural network for substation-level voltage estimation that exploits both electrical topology and physical features, while remaining robust to the low observability levels common to real-world distribution networks. Leveraging the public SMART-DS datasets, the model is trained and evaluated on thousands of buses across multiple substations and DER penetration scenarios. Comprehensive experiments demonstrate that the proposed method achieves up to 2 times lower RMSE than alternative data-driven models, and maintains high accuracy with as little as 1\% measurement coverage. The results highlight the potential of GNNs to enable scalable, reproducible, and data-driven voltage monitoring for distribution systems.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。