arXiv：cs.LG· Olayiwola Arowolo, Oluwadara Adedeji, Peter Palensky, Jochen L. Cremer·· 4 小时前
TabPFN 无需微调实现电网稳态分析，覆盖潮流预测与 N-k 故障筛选
Learning by Examples: Power Grid Steady State Analysis with a Tabular Foundation Model
AI 导读
预训练表格基础模型 TabPFN 无需重新训练或微调，即可作为电网稳态分析的通用神经求解器，在 118 与 2000 节点系统上匹配或超越任务专用神经基线。在 118 节点潮流预测中，其电压幅值误差为 1×10⁻³ pu、电压相角误差为 0.30°，优于所有基线；对数百万未见的 N-2 故障，支路与电压越限筛选 AUC 分别达 0.92 和 0.95。
正文
Abstract:Foundation models have shown strong generalization across scientific domains, but their use in power systems remains largely task-specific. We investigate whether a pretrained tabular foundation model (TFM), TabPFN, can serve as a general-purpose neural solver for power grid steady-state analysis. We evaluate the TFM on three tasks: power flow (PF) prediction, N-k contingency screening (NCS), and voltage stability margin (VSM) prediction. We performed experiments on 118- and 2000-bus systems, comparing the TFM against task-specific neural baselines. Without retraining or fine-tuning, the TFM matches or outperforms the best task-specific model in several settings across the three tasks. For 118-bus PF prediction, a variant of the TFM achieves errors of $1\times 10^{-3}$ pu for voltage magnitudes and $0.30^\circ$ for voltage angles, outperforming all baselines. On unseen N-1 contingency and close-to-infeasible PF cases, the TFM is competitive with the DCPF approximation. For millions of unseen N-2 contingencies, the TFM achieves AUCs of 0.92 and 0.95 for branch and voltage violation screening, respectively. The TFM also attains accurate VSM prediction performance for the 2000-bus system with limited labelled data. The TFM attains strong results across the tested tasks despite pretraining without any power-system-specific dataset and requiring no fine-tuning step. These results offer a glimpse into the potential of pretrained TFMs as a neural solver for a range of power grid analysis tasks.
| Comments: | 12 pages, 8 figures, under review |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.20462 [cs.LG] |
| (or arXiv:2610.20462v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.20462 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Olayiwola Arowolo Mr [view email]
[v1]
Wed, 14 Oct 2026 16:44:29 UTC (448 KB)
来源：arXiv：cs.LG · arxiv.org