Research on a wind power operation and maintenance Q&A system based on large language models and knowledge graphs

doi:10.3969/j.issn.2097-0706.2024.09.008

Abstract

Abstract:

The operation and maintenance of wind farms heavily rely on on-site practical experience， while the high turnover rate in the industry poses challenges to the impartment of such experience. Traditional knowledge bases and Q&A systems are increasingly revealing their limitations in this regard. To enhance the applicability and reliability of Q&A systems in professional domains， this paper designs a wind farm operation and maintenance Q&A system that integrates large language models （LLMs） with knowledge graphs. Through semantic understanding and correlation analysis， the system combines both structured and unstructured data to provide comprehensive and accurate professional responses. Both subjective and objective evaluations indicate that the accuracy， coherence， and informativeness of this specialized Q&A model surpass those of a certain Chinese LLM and the ChatGLM model. This not only improves the efficiency of wind farm operation and maintenance but also offers a solution for knowledge transfer and updating within the industry.

Key words: wind operation and maintenance, large language model, knowledge graph, dual-based Q&A system, ChatGLM model

CLC Number:

TK81：TP24

CHEN Qing, LIU Yusheng, DUAN Lianda, LIANG Hao, SUN Qitao, LU Nana. Research on a wind power operation and maintenance Q&A system based on large language models and knowledge graphs[J]. Integrated Intelligent Energy, 2024, 46(9): 61-68.

Figures/Tables 9

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数据来源	描述	数据/条
故障工单&预警工单	包含风电机组触发的故障运维、预警工单信息，包括故障现象、处理手段等经验知识	10 725
质量反馈单	涵盖现场到场处理的工单信息和物料备件信息	9 424
故障代码手册	涵盖各类风电机组的触发逻辑条件	1 962
故障处理手册	涵盖风电机组运维积累的故障分析树知识	368
风电机组电子档案	包含风电机组基础信息，如部件品牌、规格等	250 000

模型	余弦距离中位数	相似度中位数	R₅₀/%
某中文LLM	0.819	0.530	52
ChatGLM	0.815	0.590	66
专业问答	0.821	0.885	90