基于BERT模型的主设备缺陷诊断方法研究  

Research on primary equipment defect diagnosis method based on the BERT model

作　　者：杨虹 孟晓凯 俞华 白洋 韩钰 刘永鑫 YANG Hong;MENG Xiaokai;YU Hua;BAI Yang;HAN Yu;LIU Yongxin(State Grid Shanxi Electric Power Research Institute,Taiyuan 030002,China)

机构地区：[1]国网山西省电力公司电力科学研究院,山西太原030002

出　　处：《电力系统保护与控制》2025年第7期155-164,共10页Power System Protection and Control

基　　金：国家自然科学基金面上项目资助(62176227);国网山西省电力公司科技项目资助(52053023000P)。

摘　　要：主设备缺陷诊断旨在及时定位处理电网的异常情况,是电力系统平稳运行的基础。传统方法以人工为主,存在效率低下、诊断成本高、依赖专家经验等问题。为了弥补这些不足,提出了一种基于BERT语言模型的主设备缺陷诊断方法。首先,使用BERT初步理解输入,获取嵌入表示,结合缺陷等级分类任务判断故障的危急程度。然后,利用大语言模型汇总输入信息和评判结果,并通过大语言模型提示学习提高知识问答过程的准确性与推理可靠性,返回正确有效的回答。最后,探究了大语言模型在电力领域的应用潜力。实验结果表明,所提方法在缺陷等级分类任务和问答任务上都表现良好,可以生成高质量的分类证据和指导信息。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.

关 键 词：缺陷诊断 大语言模型 BERT 提示学习 分类模型 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]