PhyPro-FM: A Physics and Prompt-Guided Graph Foundation Model for Contingency Analysis
The continuous evolution of next-generation power systems, characterized by the high penetration of renewable energy resources and dynamic topology variations, poses formidable challenges to traditional N-1/N-2 contingency analysis in terms of computational efficiency and scalability. While deep learning-based methods offer accelerated inference, existing studies predominantly treat contingency analysis as a pure regression problem, often overlooking the physical essence of grid operation and engineering requirements of real-world scenarios. To bridge this gap, this paper proposes a physics and prompt-guided graph foundation model (PhyPro-FM) for generalizable and engineering-viable conti...
| Authors | Xu Yang, Zhanyuan Sheng, Chenhui Lin et al. |
|---|---|
| Primary category | eess.SY |
| Published | 19 October 2026 |
| Last updated at source | 19 October 2026 |
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