Junpeng Ding, Zichen Tang, Haihong E, Mengyuan Ji, Yang Liu, Haolin Tian, Haiyang Sun, Pengqi Sun, Yang Xu, Yichen Liu, Haocheng Gao, Zijie Xi, Ruomeng Jiang, Peizhi Zhao, Rongjin Li, Yuanze Li, Jiacheng Liu, Zhongjun Yang, Jintong Chen, Siying Lin. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

To address the inadequacy of conventional emergency generator tripping (EGT) in adapting to the topological changes in power systems, this paper proposes a novel knowledgeguided transfer reinforcement learning framework. For training efficiency optimization, we architect a knowledge-embedded action guidance module that incorporates invalid action masking and critical action weighting. This innovative approach transforms raw EGT action into optimized control commands, ensuring physical rationality while reducing ineffective exploration and improving decision-making efficiency. To enhance model adaptability to topology variations, we propose a knowledge-informed spatiotemporal transformer (KISTT) driven proximal policy optimization (PPO) architecture. The framework integrates the KISTT module as the PPO’s feature extraction layer, which effectively captures the spatiotemporal correlations by incorporating expert knowledge, enabling accurate perception of topological changes in EGT scenarios. Regarding model degradation to overload operations and topological variations, we propose a hybrid model transfer framework that facilitates adaptive parameter transfer from source to target domain, thereby significantly enhancing convergence speed and control performance in the target domain. Simulation results on the IEEE 39-bus system and Northeast China Power Grid demonstrate that the proposed method outperforms existing approaches in control performance, model adaptability and transferability, providing an innovative solution for EGT in dynamic grid environments.