Knowledge-GPT Guided Generalizable Reinforcement Learning for Intelligent Emergency Generator Tripping in Power System

Emergency control is essential for ensuring transient stability in power systems after faults. This study addresses the limitations in existing methods by proposing a knowledge-generative pretrained transformer (GPT)-guided generalizable reinforcement learning (RL) approach for intelligent emergency generator tripping. This approach incorporates general electrical principles and knowledge-GPT to assist deep reinforcement learning (DRL). The general electrical principles involve identifying severely disturbed generators and selecting appropriate control actions through dynamic probability. The knowledge-GPT model extracts insights from an expert strategy knowledge base, reshaping the DRL reward structure by comparing the DRL strategy with the knowledge-GPT outputs. This paradigm is designed to leverage electrical laws and domain expertise to guide the DRL training process, thereby enhancing both training efficiency and electrical consistency. To enhance generalization capability under topological changes, message passing neural networks (NNs) are integrated into the DRL architecture, effectively simulating power flow dynamics in transmission lines. The proposed method is validated through simulations on the IEEE 39-bus system and the Northeast power grid of China, demonstrating superior control effectiveness and adaptability compared to existing approaches, offering a more robust solution for emergency control in complex power systems.