Physics guided graph model improves power grid contingency analysis
PhyPro-FM: A Physics and Prompt-Guided Graph Foundation Model for Contingency Analysis
Artificial IntelligenceMachine Learning
Summary
Power grids with many renewable energy sources change rapidly, making it hard to check for problems quickly and accurately. The authors created a new model called PhyPro-FM that understands the physical rules of electricity flows and uses past data to better predict how the grid reacts to failures. This model learns more effectively than previous methods and can handle new grid setups it hasn't seen before. It also includes fixes to avoid unstable predictions, making it practical for real-world use.
What this means in practice
- •For power grid operators: Improve prediction accuracy and reliability of contingency scenarios under changing grid conditions to enhance operational security.
- •For energy management software developers: Integrate physics-informed graph models for more scalable and generalizable contingency analysis tools supporting renewable-heavy grids.
Authors
Xu Yang, Zhanyuan Sheng, Chenhui Lin, Haotian Liu, Qi Wang, Yue Yang, Wenchuan Wu
Abstract
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 contingency analysis. The proposed architecture integrates graph neural networks with a learnable global query mechanism and, notably, introduces a prompt-guided learning paradigm. By leveraging historical snapshots as few-shot prompts, the model dynamically captures correlations between target and historical operating conditions, significantly enhancing generalization capabilities under unseen topologies. Furthermore, we design a physics-informed training mechanism that prioritizes branch power flow errors over nodal voltage errors, aligning the optimization objective with critical grid security constraints. To ensure engineering applicability, a physics-informed inference mechanism is also developed, incorporating a residual-based correction module and a specialized post-processing strategy for singular branches to circumvent numerical instability. Comprehensive experiments demonstrate that PhyPro-FM outperforms state-of-the-art methods in accuracy and generalization, validating its superior physical consistency and robustness for practical engineering applications.