让机器学习模型在推理时自动修正电力潮流计算，更准更合规。
Test Time Training for AC Power Flow Surrogates via Physics and Operational Constraint Refinement
- 推理时通过梯度更新微调输出，强制符合物理方程和运行约束。
- 在多个电网系统上使误差和违规降低一到两个数量级。
- 无需标注数据，适合需要快速准确且物理可信的电力系统场景。
基于机器学习的电力潮流（PF）计算虽比传统数值方法快得多，但常缺乏物理一致性。本文提出一种物理引导的测试时训练（PI-TTT）框架，在推理阶段直接施加交流潮流方程和运行约束，通过少量梯度更新实现轻量级自监督修正，使模型能适应未见运行工况，且无需标签数据。在IEEE 14、118、300节点系统及PEGASE 1354节点网络上的实验表明，与纯机器学习模型相比，PI-TTT将潮流残差和运行约束违反降低了1至2个数量级，同时保持其计算高效性。结果证明，PI-TTT能提供快速、准确且物理可靠的预测，是电力系统分析中可扩展、物理一致学习的有前景方向。
原文摘要 · Abstract (English)
Power Flow (PF) calculation based on machine learning (ML) techniques offer significant computational advantages over traditional numerical methods but often struggle to maintain full physical consistency. This paper introduces a physics-informed test-time training (PI-TTT) framework that enhances the accuracy and feasibility of ML-based PF surrogates by enforcing AC power flow equalities and operational constraints directly at inference time. The proposed method performs a lightweight self-supervised refinement of the surrogate outputs through few gradient-based updates, enabling local adaptation to unseen operating conditions without requiring labeled data. Extensive experiments on the IEEE 14-, 118-, and 300-bus systems and the PEGASE 1354-bus network show that PI-TTT reduces power flow residuals and operational constraint violations by one to two orders of magnitude compared with purely ML-based models, while preserving their computational advantage. The results demonstrate that PI-TTT provides fast, accurate, and physically reliable predictions, representing a promising direction for scalable and physics-consistent learning in power system analysis.
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。