Primary equipment defect diagnosis aims to promptly locate and address abnormal situations in the power grid

serving as a foundation for the stable operation of the power system. Traditional methods rely heavily on manual efforts

leading to low efficiency

high diagnostic costs

and dependence on expert experience. To overcome these limitations

this paper proposes a primary equipment defect diagnosis method based on language models such as BERT. First

the BERT model is employed to preliminarily comprehend the input and obtain embedded representations

which are then used in the defect level classification task to assess the severity of the defect. Subsequently

a large language model is utilized to consolidate the input information and classification results

improving the accuracy and reasoning reliability of the knowledge-based Q&A process through prompt learning

thereby providing correct and effective answers. Finally

the potential applications of large language models in the power industry are explored. Experimental results demonstrate outstanding performance of this method in both defect level classification and question-answering tasks

generating high-quality classification evidence and guidance information.