Lightweight model for power grid cascading failures risk evaluation based on graph physics-informed attention network

Yang, Kehao, Xue, Fei, Huang, Tao, Lu, Shaofeng, Jiang, Lin ORCID: 0000-0001-6531-2791 and Xu, Xu (2025) Lightweight model for power grid cascading failures risk evaluation based on graph physics-informed attention network EXPERT SYSTEMS WITH APPLICATIONS, 291. 128468-. ISSN 0957-4174, 1873-6793

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Abstract

In modern power grids, cascading failures pose an escalating threat to grid reliability, leading to the importance of predicting the likelihood of such failures. While existing power flow-based models rely on detailed physical dynamics, their computational latency hinders online applications. This study introduces a lightweight Graph Physics-Informed Attention Network (GPIAN), uniquely integrating power grid physical laws with graph neural network attention to address this gap. GPIAN replaces conventional attention mechanism with a complex network-based framework, where the Electric Functional Strength (EFS), a metric quantifying node interaction guided by power grid principles, drives adaptive information aggregation. This design not only reduces model parameters by 90.7% compared to standard graph attention network but also embeds physical interpretability, enabling the model to prioritize critical node-edge dependencies in cascading failure scenarios. Experimental validation across IEEE-39, IEEE-118, IEEE-300, and Italian power grids demonstrates that GPIAN achieves higher prediction accuracy than mainstream methods, while maintaining fast inference speeds suitable for real-time deployment. These results highlight how merging physical principles with data-driven learning can transform cascading failure prediction, offering a practical, interpretable tool for proactive grid management and significantly advancing the field's capacity to mitigate blackout risks.

Item Type:	Article
Uncontrolled Keywords:	Cascading failures, Power grid, Physics-informed, Complex network, Graph classification
Divisions:	Faculty of Science & Engineering Faculty of Science & Engineering > School of Engineering Faculty of Science & Engineering > School of Engineering > Electrical Engineering and Electronics
Depositing User:	Symplectic Admin
Date Deposited:	04 Mar 2026 14:23
Last Modified:	16 Jun 2026 17:25
DOI:	10.1016/j.eswa.2025.128468
Related Websites:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3197345
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