Volume 68

A Zero-Shot LLM Learning Framework for Switching Sequence Generation in Distribution Network Service Restoration Zhiqun Zhang, Bing Sun,Hao Yu, Haoran Ji, Guanyu Song, Jinli Zhao, Peng Li

Abstract

Service restoration after fault isolation is critical to distribution network reliability, yet existing reconfiguration research stops at the final topology and overlooks the switching sequence that dispatch actually requires. This paper proposes a zero-shot LLM reasoning framework that generates an ordered, physically feasible switching sequence toward a target topology. Graph-theoretic topology logic and device constraints are embedded into prompts; a Chain-of-Thought paradigm decomposes the task into fault isolation, topology and outage analysis, service restoration, and load transfer; and a self-reflection mechanism validates feasibility and topological compliance. Validation on the IEEE 33-bus system confirms the framework’s effectiveness and interpretability.

Keywords distribution network service restoration, switching operation sequence, large language model, prompt engineering, zero-shot reasoning