Research on Maintenance Strategy Generation of Substation Equipment Based on Large Model Retrieval Enhancement Technology

With the continuous expansion of power grid scale and the increasing complexity of power equipment, substation equipment maintenance decision-making faces challenges such as big data challenges, information complexity, and high decision-making barriers. Traditional maintenance strategy generation methods mainly rely on human expertise and domain knowledge, which are not only inefficient but also easily affected by human factors, leading to inaccurate and delayed decision-making. In recent years, with the rapid development of Natural Language Processing (NLP) technology, especially the emergence of large language models (LLMs), a innovative methodology for the automatic generation of substation equipment maintenance strategies has been provided. This study presents a novel methodology for generating substation maintenance strategies through retrieval-augmented large language model technology By constructing a retrieval-augmented knowledge repository containing a vast amount of historical maintenance cases and professional knowledge, and utilizing the generation capability of large language models, the method achieves rapid and accurate diagnosis of substation equipment faults and automatic generation of maintenance strategies. Empirical evaluations demonstrate that this method can significantly improve the efficiency and accuracy of maintenance strategy generation, providing strong technical support for the stable operation of the power system.