A Conversational Agent based on Large Language Models for Fault Recovery Planning Generation

With economic development and the increasing electricity demand, distribution network operation has become indispensable for maintaining the power system reliability. However, fault recovery planning for distribution network still faces challenges such as human error, redundant workflows, and duplicated work. Large language models (LLMs), which have exceptional semantic understanding and automated generation capabilities, have recently attracted more and more attention. In this paper, we propose a novel conversational agent based on the mainstream LLMs for fault recovery plan generation. Besides, we introduce a novel tool-learning method that integrates various functionalities, encompassing topology querying, power flow calculations, and formatted text generation. Experiments demonstrate that the fault recovery plan generation agent can effectively leverage the integrated tools, achieving an average success rate of 99.25% in tool invocation.