AI 中文总结
本文提出下垂感知的GridFM扩展，将下垂和死区参数作为节点特征嵌入，实现控制感知的交流潮流分析，并在多母线系统上验证了高精度与可扩展性。
AI 中文摘要
本文开发了GridFM电力系统基础模型的下垂感知扩展，将下垂增益和频率/电压死区参数作为每个母线的节点特征嵌入，以实现控制感知的交流潮流分析。现有的潮流数据集仅编码静态电气特征，混淆了来自不同控制机制定性不同的运行点；本工作通过将下垂和死区参数暴露为结构化节点特征来解决这一差距，其中死区不连续性通过平滑的tanh近似处理，以保持求解器的可微性。一个基于Transformer的图神经网络在掩码重建上预训练，并在生成的控制感知数据集上微调。该框架在双母线系统上针对PSCAD电磁暂态仿真进行了验证（最大稳态误差0.11%），并在IEEE 24母线RTS上针对独立的PyPower下垂求解器进行了交叉验证。在24母线系统上，代理模型在有功发电方面达到R2=0.9996，电压幅值RMSE为0.0015 p.u.；在IEEE 300母线系统上确认了可扩展性（电压幅值RMSE为0.0036 p.u.，R2=0.9841，涵盖299,700个预测）。一项三模式控制研究进一步表明，死区加宽了控制误差分布，同时保持总下垂补偿不变，确立了死区宽度作为可操作的节点级设计特征。
英文摘要
This paper develops a droop-aware extension of the GridFM power systems foundation model, embedding droop gains and frequency/voltage deadband parameters as per-bus node features to enable control-aware AC power-flow analysis. Existing power-flow datasets encode only static electrical features, conflating operating points from qualitatively different control regimes; this work resolves that gap by exposing droop and deadband parameters as structured node features, with deadband discontinuities handled through a smooth tanh approximation that preserves solver differentiability. A transformer-based graph neural network is pre-trained on masked reconstruction and fine-tuned on the resulting control-aware datasets. The framework is validated against PSCAD electromagnetic-transient simulations on a two-bus system (0.11% maximum steady-state error) and cross-validated against an independent PyPower droop solver on the IEEE 24-bus RTS. On the 24-bus system the surrogate attains R2 = 0.9996 for active generation and 0.0015 p.u. voltage-magnitude RMSE; scalability is confirmed on the IEEE 300-bus system (0.0036 p.u. RMSE, R2 = 0.9841 for voltage magnitude across 299,700 predictions). A three-mode control study further shows that the deadband widens the control-error distribution while leaving total droop compensation unchanged, establishing deadband width as an actionable node-level design feature.
