Technical Challenges and Development Pathway of Intelligent Maintenance in Power Equipment Using Large Language Model

LI Yuan; LIN Jinshan; BIAN Chang; FANG Qixuan; LIU Zhihao; ZHANG Guanjun

doi:null

您当前的位置：

首页 >

文章列表页 >

Technical Challenges and Development Pathway of Intelligent Maintenance in Power Equipment Using Large Language Model

更新时间：2026-06-23

- Technical Challenges and Development Pathway of Intelligent Maintenance in Power Equipment Using Large Language Model
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY (2026)
- 作者机构：
  
  西安交通大学电工材料电气绝缘全国重点实验室，710049，西安
- 作者简介：
- 基金信息：
- DOI：
  CLC： TM83
- Received：12 April 2026，
  
  Revised：2026-06-17，
  
  Accepted：23 June 2026，
- 稿件说明：
移动端阅览
LI Yuan, LIN Jinshan, BIAN Chang, et al. Technical Challenges and Development Pathway of Intelligent Maintenance in Power Equipment Using Large Language Model[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026.
DOI：

LI Yuan, LIN Jinshan, BIAN Chang, et al. Technical Challenges and Development Pathway of Intelligent Maintenance in Power Equipment Using Large Language Model[J/OL]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY, 2026. DOI：

摘要

随着我国新型电力系统加快建设，分布式新能源和新型负荷规模化接入电网，电力设备承受着宽频谐波扰动、高负荷波动等复杂应力作用，这对设备的运行可靠性和运维技术提出了更高要求。针对电力设备智能运维中多源异构数据融合困难、专业知识迁移不足、复杂故障推理可信性不强等问题，系统研究了大模型赋能电力设备智能运维的关键技术与发展路径。梳理了电力设备智能运维大模型的关键技术及发展路径，构建了涵盖数据、感知、认知与决策等模块的电力设备智能运维大模型框架。结合全域数据驱动、运行优化协同和智能化运检支持等典型场景，阐明大模型在设备状态感知、任务分解、工具调用和人机协同决策中的作用机制。从数据质量与安全、模型架构与物理一致性、算力部署和多场景适配这四个方面分析当前应用挑战，并提出私有化部署、多模态语义对齐、神经-符号融合、云边端协同、大模型中枢结合小模型插件等发展路径，为电力设备智能运维大模型可信构建与工程应用提供参考。

Abstract

As China’s new-type power system accelerates its development

distributed renewable energy sources and new types of loads are being integrated into the power grid on a large scale. As a result

power equipment is subjected to complex stresses such as broadband harmonic disturbances and high load fluctuations

which impose higher requirements on operational reliability and maintenance technologies. To address the challenges in intelligent operation and maintenance of power equipment—such as the difficulty in fusing multi-source heterogeneous data

insufficient transfer of domain knowledge

and the lack of trustworthiness in complex fault reasoning—this paper systematically investigates the key technologies and development pathways of large model-empowered intelligent operation and maintenance for power equipment. It outlines the key technologies and development paths of large models in this domain and constructs a large model framework for intelligent operation and maintenance of power equipment

comprising modules such as data

perception

cognition

and decision-making. By drawing on typical scenarios such as globally data-driven operation

operation optimization coordination

and intelligent operation and inspection support

the paper elucidates the mechanisms through which large models function in equipment state perception

task decomposition

tool invocation

and human–machine collaborative decision-making. The study further analyzes current application challenges from four perspectives: data quality and security

model architecture and physical consistency

computational resource deployment

and multi-scenario adaptation. Finally

it proposes development pathways including private deployment

multimodal semantic alignment

neural-symbolic integration

cloud–edge–device collaboration

and the combination of large model hubs with small model plug-ins

thereby providing a reference for the trustworthy construction and engineering application of large models for intelligent operation and maintenance of power equipment

关键词

Keywords

references

中共中央国务院 . 中共中央国务院关于完整准确全面贯彻新发展理念做好碳达峰碳中和工作的意见 [EB/OL ] . 北京：新华社 , 2021 .

The State Council of the People’s Republic of China . Opinions on thoroughly, accurately, and comprehensively implementing the new development philosophy and achieving carbon peaking and carbon neutrality [EB/OL ] . Beijing, China : Xinhua News Agency , 2021 .

国家能源局 . 新型电力系统发展蓝皮书 [R ] . 北京 : 国家能源局 , 2023 .

National Energy Administration . Blue Book on the Development of New Power Systems [R ] . Beijing : National Energy Administration , 2023 .

张智刚，康重庆 . 碳中和目标下构建新型电力系统的挑战与展望 [J ] . 中国电机工程学报， 2022 ， 42 ( 08 )： 2806 - 2818 .

ZHANG Zhigang , KANG Chongqing . Challenges and prospects for constructing the new-type power system towards a carbon neutrality future [J ] . Proceedings of the CSEE , 2022 , 42 ( 08 ): 2806 - 2818 .

盛戈皞 , 钱勇 , 罗林根 , 等 . 面向新型电力系统的数字化电力设备关键技术及其发展趋势 [J ] . 高电压技术 , 2023 , 49 ( 05 ): 1765 - 1778 .

SHENG Gehao , QIAN Yong , LUO Lingen , et al . Key Technologies and Development Trends of Digital Power Equipment for New Type Power System [J ] . High Voltage Engineering , 2023 , 49 ( 05 ): 1765 - 1778 .

谢小荣 , 贺静波 , 毛航银 , 等 . “双高”电力系统稳定性的新问题及分类探讨 [J ] . 中国电机工程学报 , 2021 , 41 ( 02 ): 461 - 475 .

XIE Xiaorong , HE Jingbo , MAO Hangyin , et al . New Problems and Classification of Stability in "Dual-High" Power Systems [J ] . Proceedings of the CSEE , 2021 , 41 ( 02 ): 461 - 475 .

江秀臣 , 许永鹏 , 李曜丞 , 等 . 新型电力系统背景下的输变电数字化转型 [J ] . 高电压技术 , 2022 , 48 ( 01 ): 1 - 10 .

JIANG Xiuchen , XU Yongpeng , LI Yaocheng , et al . Digital Transformation of Power Transmission and Transformation Under the Background of New Power System [J ] . High Voltage Engineering , 2022 , 48 ( 01 ): 1 - 10 .

中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要滚动新闻, 中国政府 [EB/OL ] , 2024 .

Rolling News on the Outline of the 14th Five-Year Plan for National Economic and Social Development and the Long-Range Objectives Through the Year 2035 of the People's Republic of China , The Chinese Government [EB/OL ] , 2024 .

ACHIAM J . Adler S, AGARWAL S, et al. GPT-4 technical report . arXiv: 2303.08774 , 2023 .

ANIL R , DAI A M , FIRAT O , et al . PaLM 2 technical report [J ] . arXiv: 2305.10403 , 2023 .

KATIKAPALLI Subramanyam Kalyan , AJIT Rajasekharan , Sivanesan Sangeetha . AMMUS: A Survey of Transformer-based Pretrained Models in Natural Language Processing. arXiv: 2108.05542 , 2021.

DAI Damai , DENG Chengqi , ZHAO Chenggang , et al . DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models. arXiv: 2401.06066 , 2024.

任海玉 , 刘建平 , 王健 , 等 . 基于大语言模型的智能问答系统研究综述 [J ] . 计算机工程与应用 , 2025 , 61 ( 07 ): 1 - 24 .

REN Haiyu , LIU Jianping , WANG Jian , et al . Research on Intelligent Question Answering System Based on Large Language Model：A Survey [J ] . Computer Engineering and Applications , 2025 , 61 ( 07 ): 1 - 24 .

王合庆 , 魏杰 , 景红雨 , 等 . Meta-RAG: 基于元数据驱动的电力领域检索增强生成框架 [J ] . 计算机工程 , 2026 , 52 ( 02 ): 383 - 392 .

WANG Heqing , WEI Jie , JING Hongyu , et al . Meta-RAG: A Metadata-Driven Retrieval Augmented Generation Framework for the Power Industry [J ] . Computer Engineering , 2026 , 52 ( 02 ): 383 - 392 .

阮子禅 , 包宏宇 , 王文君 . 多模态大语言模型应用于工业数据分类分级的初步研究与思考 [J ] . 工业信息安全 , 2024 , ( 06 ): 43 - 52 .

RUAN Zichan , BAO Hongyu , WANG Wenjun . An Initial Investigation on Applying Multi-modal Large Language Model on Industrial Data Classification and Grading [J ] . Industry Information Security , 2024 , ( 06 ): 43 - 52 .

马超 , 李武峰 , 陈羽飞 , 等 . ChatGPT类大语言模型赋能电力标准数字化转型的核心技术、技术特征及应用展望 [J/OL ] . 高电压技术 , 2025 , 51 ( 04 ): 1727 - 1746 .

MA Chao , LI Wufeng , CHEN Yufei , et al . The Core Technologies, Technical Characteristics, and Application Prospects of ChatGPT Class Big Language Models on the Digital Transformation of Standards in Electric Power Field [J/OL ] . High Voltage Engineering , 2025 , 51 ( 04 ): 1727 - 1746 .

丁俐夫 , 陈颖 , 肖谭南 , 等 . 基于大语言模型的新型电力系统生成式智能应用模式初探 [J ] . 电力系统自动化 , 2024 , 48 ( 19 ): 1 - 13 .

DING Lifu , CHEN Ying , XIAO Tannan , et al . Exploration of Generative Intelligent Application Mode for New Power Systems Based on Large Language Models [J ] . Automation of Electric Power Systems , 2024 , 48 ( 19 ): 1 - 13 (in Chinese) .

YAN Z M , XU Y . Real-time optimal power flow with linguistics stipulations: integrating GPT-agent and deep reinforcement learning [J ] . IEEE Transactions on Power Systems , 2024 , 39 ( 2 ): 4747 - 4750 .

于硕 , 王司宇 , 王超 , 类ChatGPT大语言模型在电力系统中的应用前景 [J ] . 电气时代， 2023 ( 10 ): 50 - 53 .

YU Shuo , WANG Siyu , WANG Chao , et al . Application prospect of ChatGPT-like large language model in power system [J ] . Electric Age , 2023 ( 10 ): 50 - 53 .

赵俊华 , 文福拴 , 黄建伟 , 等 . 基于大语言模型的电力系统通用人工智能展望：理论与应用 [J ] . 电力系统自动化 , 2024 , 48 ( 06 ): 13 - 28 .

ZHAO Junhua , WEN Fushuan , HUANG Jianwei , et al . Preliminary research on generalized artificial intelligence for power system based on large language modeling:theory and applications [J/OL ] . Automation of Electric Power Systems , 2024 , 48 ( 06 ): 13 - 28 .

高宁 , 张冠军 , 严璋 . 变电站高压电气设备绝缘诊断专家系统的开发 [J ] . 西安交通大学学报 , 1996 , ( 11 ): 82 - 87 .

GAO Ning , ZHANG Guanjun , YAN Zhang . Application of Expert System for Insulation Diagnosis of High Voltage Electrical Equipment in Substation [J ] . Journal of Xi'an Jiaotong University , 1996 , ( 11 ): 82 - 87 .

杨莉 , 周跃峰 , 尚勇 , 等 . 变压器故障诊断专家系统中的基于面向对象层次分类模型 [J ] . 高压电器 , 1999 , ( 03 ): 15 - 17+48 .

YANG Li , ZHOU Yueyong , SHANG Yong , et al . Object-Oriented Hierarchical Classification Model in Transformer Fault Diagnosis Expert System [J ] . High Voltage Apparatus , 1999 , ( 03 ): 15 - 17+48 .

蒲天骄 , 乔骥 , 韩笑 , 等 . 人工智能技术在电力设备运维检修中的研究及应用 [J ] . 高电压技术 , 2020 , 46 ( 02 ): 369 - 383 .

PU Tianjiao , QIAO Ji , HAN Xiao , et al . Research and Application of Artificial Intelligence in Operation andMaintenance for Power Equipment [J ] . High Voltage Engineering , 2020 , 46 ( 02 ): 369 - 383 .

刘云鹏 , 许自强 , 李刚 , 等 . 人工智能驱动的数据分析技术在电力变压器状态检修中的应用综述 [J ] . 高电压技术 , 2019 , 45 ( 02 ): 337 - 348 .

LIU Yunpeng , XU Ziqiang , LI Gang , et al . Review on Applications of Artificial Intelligence Driven Data Analysis Technology in Condition Based Maintenance of Power Transformers [J ] . High Voltage Engineering , 2019 , 45 ( 02 ): 337 - 348

盛戈皞 , 钱勇 , 罗林根 , 等 . 面向新型电力系统的电力设备运行维护关键技术及其应用展望 [J ] . 高电压技术 , 2021 , 47 ( 09 ): 3072 - 3084 .

SHENG Gehao , QIAN Yong , LUO Lingen , et al . Key Technologies and Application Prospects for Operation and Maintenance of Power Equipment in New Type Power System [J ] . High Voltage Engineering , 2021 , 47 ( 09 ): 3072 - 3084 .

蒲天骄 , 赵琦 , 王新迎 . 电力人工智能技术研究框架、应用现状及展望 [J ] . 电网技术 , 2025 , 49 ( 05 ): 1751 - 1770 .

PU Tianjiao , ZHAO Qi , WANG Xinying . Technology Framework, Application Status and Prospects on Electric Power Artificial Intelligence [J ] . Power System Technology , 2025 , 49 ( 05 ): 1751 - 1770 .

李旭斌 , 田付强 , 郭亦可 . 新型电力系统中电力设备健康管理与智能运维关键技术探究 [J ] . 电网技术 , 2023 , 47 ( 09 ): 3710 - 3727 .

LI Xubin , TIAN Fuqiang , GUO Yike . Key Technologies for Health Management and Intelligent Operation and Maintenance of Power Equipment in New Power Systems [J ] . Power System Technology , 2023 , 47 ( 09 ): 3710 - 3727 (in Chinese) .

范士雄 , 赵泽宁 , 郭剑波 , 等 . 数据驱动的电力系统暂态稳定评估方法综述 [J ] . 中国电机工程学报 , 2024 , 44 ( 09 ): 3408 - 3429 .

FAN Shixiong , ZHAO Zening , GUO Jianbo , et al . Review on Data-driven Power System Transient Stability Assessment Technology [J ] . Proceedings of the CSEE , 2024 , 44 ( 09 ): 3408 - 3429 .

韩富佳 , 王晓辉 , 乔骥 , 等 . 基于人工智能技术的新型电力系统负荷预测研究综述 [J ] . 中国电机工程学报 , 2023 , 43 ( 22 ): 8569 - 8592 .

HAN Fujia , WANG Xiaohui , QIAO Ji , et al . Review on Artificial Intelligence Based Load Forecasting Research forthe New-type Power System [J ] . Proceedings of the CSEE , 2023 , 43 ( 22 ): 8569 - 8592 .

唐文虎 , 牛哲文 , 赵柏宁 , 等 . 数据驱动的人工智能技术在电力设备状态分析中的研究与应用 [J ] . 高电压技术 , 2020 , 46 ( 09 ): 2985 - 2999 .

TANG Wenhu , NIU Zhewen , ZHAO Boning , et al . Research and Application of Data-driven Artificial Intelligence Technology for Condition Analysis of Power Equipment [J ] . High Voltage Engineering , 2020 , 46 ( 09 ): 2985 - 2999 .

李刚 , 张博 , 赵文清 , 等 . 电力设备状态评估中的数据科学问题: 挑战与展望 [J ] . 电力系统自动化 , 2018 , 42 ( 21 ): 10 - 20+177 .

LI Gang , ZHANG Bo , ZHAO Wenqing , et al . Data Science Issues in State Evaluation of Power Equipment: Challenges and Prospects [J ] . Automation of Electric Power Systems , 2018 , 42 ( 21 ): 10 - 20+177 .

江秀臣 , 盛戈皞 . 电力设备状态大数据分析的研究和应用 [J ] . 高电压技术 , 2018 , 44 ( 04 ): 1041 - 1050 .

JIANG Xiuchen , SHENG Gehao . Research and Application of Big Data Analysis of Power Equipment Condition [J ] . High Voltage Engineering , 2018 , 44 ( 04 ): 1041 - 1050 .

莫娟 , 王雪 , 董明 , 等 . 基于粗糙集理论的电力变压器故障诊断方法 [J ] . 中国电机工程学报 , 2004 , ( 07 ): 166 - 171 .

MO Juan , WANG Xue , DONG Ming , et al . Diagnostic Model of Insulation Faults in Power Equipment Based on Rough Set Theory [J ] . Proceedings of the CSEE , 2004 , ( 07 ): 166 – 171 .

徐尧宇 , 李元 , 王怡静 , 等 . 信息缺失下变压器状态预警方法 [J ] . 高电压技术 , 2020 , 46 ( 09 ): 3062 - 3069 .

XU Yaoyu , LI Yuan , WANG Yijing , et al . Early-warning Methods of Transformer Conditions Under Information Deficiency [J ] . High Voltage Engineering , 2020 , 46 ( 09 ): 3062 - 3069 .

TANG W H , SPURGEON K , Wu Q H , et al . An Evidential Reasoning Approach to Transformer Condition Assessments [J ] . IEEE Transactions on Power Delivery , 2004 , 19 ( 4 ): 1696 – 1703 .

Zhang Y , Ding X , Liu Y , et al . An Artificial Neural Network Approach to Transformer Fault Diagnosis [J ] . IEEE Power Engineering Review , 1996 , 16 ( 10 ): 55 – 55 .

曲岳晗 , 赵洪山 , 马利波 , 等 . 多深度神经网络综合的电力变压器故障识别方法 [J ] . 中国电机工程学报 , 2021 , 41 ( 23 ): 8223 - 8231 .

QU Yuehan , ZHAO Hongshan , MA Libo , et al . Multi-depth Neural Network Synthesis Method for Power Transformer Fault Identification [J ] . Proceedings of the CSEE , 2021 , 41 ( 23 ): 8223 – 8231 .

李刚 , 于长海 , 刘云鹏 , 等 . 电力变压器故障预测与健康管理: 挑战与展望 [J ] . 电力系统自动化 , 2017 , 41 ( 23 ): 156 – 167 .

LI Gang , YU Changhai , LIU Yunpeng , et al . Challenges and Prospects of Fault Prognostic and Health Management for Power Transformer [J ] . Automation of Electric Power Systems , 2017 , 41 ( 23 ): 156 – 167 .

陈翔宇 , 罗怿 , 胡军 , 等 . 新一代智能变电站变电设备状态评估大数据分析应用展望 [J ] . 电力信息与通信技术 , 2016 , 14 ( 08 ): 46 - 51 .

CHEN Xiangyu , LUO Yi , HU Jun , et al . The Prospect for Big Data Mining Application of Substation Equipment Condition Assessment in New Generation Smart Substation [J ] . Electric Power Information and Communication Technology , 2016 , 14 ( 08 ): 46 - 51 .

任建强 , 王璐阳 , 吴家浩 , 等 . 基于情景构建与推演的新能源电力设备运行风险评估 [J ] . 清华大学学报(自然科学版) , 2022 , 62 ( 10 ): 1571 - 1578 .

REN Jianqiang , WANG Luyang , WU Jiahao , et al . Operating risk assessments of new energy power equipment based on scenario construction and deduction [J ] . Journal of Tsinghua University (Science and Technology) , 2022 , 62 ( 10 ): 1571 - 1578 .

徐凯 , 梁志坚 , 张镱议 , 等 . 基于GoogLeNet Inception-V3模型的电力设备图像识别 [J ] . 高压电器 , 2020 , 56 ( 09 ): 129 - 135+143 .

XU Kai , LIANG Zhijian , ZHANG Yiyi , et al . Image Recognition of Electric Equipment Based on GoogLeNet Inception-V3 Model [J ] . High Voltage Apparatus , 2020 , 56 ( 09 ): 129 - 135+143 .

蒋逸雯 , 李黎 , 李智威 , 等 . 基于深度语义学习的电力变压器运维文本信息挖掘方法 [J ] . 中国电机工程学报 , 2019 , 39 ( 14 ): 4162 - 4171

JIANG Yiwen , LI Li , LI Zhiwei , et al . An information mining method of power transformer operation and maintenance texts based on deep semantic learning [J ] . Proceedings of the CSEE , 2019 , 39 ( 14 ): 4162 - 4171 .

李元 , 李睿 , 林金山 , 等 . 基于字词混用集成模型的电力变压器缺陷记录文本挖掘方法 [J ] . 电力工程技术 , 2024 , 43 ( 06 ): 153 - 162 .

LI Yuan , LI Rui , LIN Jinshan , et al . Character-word level ensemble integrated model for power transformer defect recording text mining method [J ] . Electric Power Engineering Technology , 2024 , 43 ( 06 ): 153 - 162 .

NELWAMONDO F V , MOHAMED S , Marwala T . Missing Data: A Comparison of Neural Network and Expectation Maximisation Techniques [J ] . Current Science , 2007 , 93 ( 11 ): 1 – 24 .

ZHANG S , OMER A M , TAO N , et al . Swin transformer network leveraging multi-dimensional features for defect depth prediction [J ] . Infrared Physics & Technology , 2024 , 139 : 105288 .

DAENENS S , VERSTRAETEN T , DAEMS P J , et al . Spatio-temporal graph neural networks for power prediction in offshore wind farms using scada data [J ] . Wind Energy Science Discussions , 2025 , 10 ( 6 ): 1137 - 1152 .

HA V N , YOUN Y W , Choi H S , et al . Self-Supervised Asynchronous Federated Learning for Diagnosing Partial Discharge in Gas-Insulated Switchgear [J ] . Energies , 2025 , 18 ( 12 ): 3078 .

潘云鹤 . 人工智能走向2.0 [J ] . 工程(英文) , 2016 , 2 ( 04 ): 51 - 61 .

PAN Yunhe, Heading toward artificial intelligence 2.0 [J ] . Engineering , 2016 , 2 ( 04 ): 51 - 61 (in Chinese) .

谢庆 , 王春鑫 , 李帆 , 等 . 知识及数据驱动的电力一次设备健康管理方法综述 [J ] . 高电压技术 , 2024 , 50 ( 02 ): 605 - 620 .

XIE Qing , WANG Chunxin , LI Fan , et al . Review of Knowledge and Data-driven Primary Equipment Health Management Methods [J ] . High Voltage Engineering , 2024 , 50 ( 02 ): 605 - 620 .

马润泽 , 王龙响 , 余佳文 , 等 . 考虑历史缺陷文本信息的断路器状态评价研究 [J ] . 机电工程 , 2015 , 32 ( 10 ): 1375 - 1379 .

MA Runze , WANG Longxiang , YU Jiawen , et al . Circuit breakers' condition evaluation considering the information in historical defect texts [J ] . Journal of Mechanical & Electrical Engineering , 2015 , 32 ( 10 ): 1375 - 1379 .

李刚 , 李银强 , 王洪涛 , 等 . 电力设备健康管理知识图谱: 基本概念、关键技术及研究进展 [J ] . 电力系统自动化 , 2022 , 46 ( 3 ): 1 - 13 .

LI Gang , LI Yingiang , WANG Hongtao , et al . Knowledge graph of power equipment health management: basic concepts, key technologies and research progress [J ] . Automation of Electric Power Systems , 2022 , 46 ( 3 ): 1 - 13 .

WANG C , CHEN Z , ZHANG Z , et al . Power Transformer Fault Prediction Based on Knowledge Graphs [J ] . arXiv: 2402.07283 , 2024 .

YU Z , ZHOU J , WANG Q , et al . Construction of knowledge graph for gas polyethylene pipelines based on ALBERT-BiGRU-CRF [J ] . Scientific Reports , 2025 , 15 ( 01 ): 25002 .

LI W , LIU X , WANG W , et al . Dynamic Fault Analysis in Substations Based on Knowledge Graphs [J ] . arXiv: 2311.13708 , 2023 .

车万翔 , 窦志成 , 冯岩松 , 等 . 大模型时代的自然语言处理:挑战、机遇与发展 [J ] . 中国科学:信息科学 , 2023 , 53 ( 09 ): 1645 - 1687 .

CHE Wanxiang , DOU Zhicheng , FENG Yansong , et al . Natural Language Processing in the Large Model Era: Challenges, Opportunities and Development [J ] . Scientia Sinica Informationis , 2023 , 53 ( 09 ): 1645 - 1687 .

胡伟 , 邹江 , 胡耀蓉 , 等 . 基于华为盘古大模型的光伏场站智能运维系统设计及实现 [J ] . 电工技术 , 2024 , ( 23 ): 122 - 126 .

HU Wei , ZOU Jiang , HU Yaorong , et al . Design and implementation of intelligent operation and maintenance system for photovoltaic power stations based on Huawei Pangu model [J ] . Electric Engineering , 2024 , ( 23 ): 122 - 126 .

孙秋野 , 张瑞霞 , 陈东岳 . 大模型技术赋能电力系统的应用及技术路线展望 [J/OL ] . 电力系统自动化 , 1 - 22 [ 2026-05-10 ] .

SUN Qiuye , ZHANG Ruixia , CHEN Dongyue . Application and route prospect of empowering power systems with large language models [J/OL ] . Automation of Electric Power Systems , 1 - 22 [ 2026-05-10 ] .

杨浩蓝 , 刘友波 , 李争博 , 等 . 新型配电系统运行专用大模型关键技术展望 [J/OL ] . 中国电机工程学报 , 1 - 23 [ 2026-05-10 ] .

YANG Haolan , LIU Youbo , LI Zhengbo , et al . Key technologies of specialized large language model for operation of next-era distribution system: a prospective study [J/OL ] . Proceedings of the CSEE , 1 - 23 [ 2026-05-10 ] .

王飞跃 . 我国生成式人工智能的发展现状与趋势 [J ] . 中国信息界 , 2025 , ( 02 ): 1 - 6 .

WANG Feiyue . The Current Development Status and Trends of Generative Artificial Intelligence in China [J ] . Information China , 2025 , ( 02 ): 1 - 6 .

王红茹 , 宋杰 . “AI+”改变千行百业 [J ] . 中国经济周刊 , 2025 , ( 13 ): 8 - 17 .

WANG Hongru , SONG Jie . AI+ Transforming Thousands of Industries [J ] . China Economic Weekly , 2025 , ( 13 ): 8 - 17 .

李鹏 , 余涛 , 李立浧 , 等 . 电力人工智能的演变与展望—从专业智能走向通用智能 [J ] . 电力系统自动化 , 2024 , 48 ( 16 ): 1 - 17 .

LI Peng , YU Tao , LI Liying , et al . Retrospect and prospect of artificial intelligence for electric power system—form domain intelligence to general intelligence [J ] . Automation of Electric Power Systems , 2024 , 48 ( 16 ): 1 - 17 .

陈晓红 , 傅文润 , 刘朝明 , 等 . 人工智能大模型在电力设备运维场景中的应用探讨 [J ] . 中国工程科学 , 2025 , 27 ( 01 ): 180 - 192 .

CHEN Xiaohong , FU Wenrun , LIU Chaoming , et al . Application of Artificial intelligence large language model in power equipment operation and maintenance [J ] . Strategic Study of CAE , 2025 , 27 ( 01 ): 180 - 192 .

YIN C , DU K , NONG Q , et al . PowerPulse: Power energy chat model with LLaMA model fine-tuned on Chinese and power sector domain knowledge . Expert Systems , 2024 , 41 ( 3 ), e13513 .

CHOI S , JAIN R , EMAMI P , et al . eGridGPT: Trustworthy AI in the Control Room [R ] . National Renewable Energy Laboratory , 2024 .

ZHANG Y , SABER A M , YOUSSEF A , KUNDUR D . Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control [R/OL ] . arXiv: 2508.05702 , 2025 .

ZHONG Yangyang , LUO Pengxin , YAN Yunfeng , et al . PowerGPT: A multimodal foundation model for power inspection [J ] . Applied Soft Computing , 2026 , 186 : 113939 .

STEPHEN H. Bach , VICTOR Sanh , YONG Zhengxin , et al . PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts [J ] . arXiv: 2202 . 01279 , 2022 .

李莉 , 时榕良 , 郭旭 , 等 . 融合大模型与图神经网络的电力设备缺陷诊断 [J ] . 计算机科学与探索 , 2024 , 18 ( 10 ): 2643 - 2655 .

LI Li , SHI Rongliang , GUO Xu , et al . Diagnosis of Power System Defects by Large Language Models and Graph Neural Networks [J ] . Journal of Frontiers of Computer Science and Technology , 2024 , 18 ( 10 ): 2643 - 2655 .

YE Z , QI D , Liu H , et al . IHGR-RAG: An Enhanced Retrieval-Augmented Generation Framework for Accurate and Interpretable Power Equipment Condition Assessment [J ] . Electronics , 2025 , 14 ( 16 ): 3284 .

LIU Y , ZHOU Y , LIU Y , et al . Intelligent fault diagnosis for CNC through the integration of large language models and domain knowledge graphs [J ] . Engineering , 2025 , 53 : 311 - 322 .

ALAWADHI S , ABBAS N . Optimizing Retrieval-Augmented Generation for Electrical Engineering: A Case Study on ABB Circuit Breakers [J ] . arXiv: 2505.17520 , 2025 .

蔡睿 , 葛军 , 孙哲 , 等 . AI预训练大模型发展综述 [J ] . 小型微型计算机系统 , 2024 , 45 ( 10 ): 2327 - 2337 .

CAI Rui , GE Jun , SUN Zhe , et al . Overview of the Development of AI Pre-trained Large Models [J ] . Journal of Chinese Computer Systems , 2024 , 45 ( 10 ): 2327 - 2337 .

WEI J , WANG X Z , SCHUURMANS D , et al . Chain of thought prompting elicits reasoning in large language models [EB/OL ] . arXiv: 2201.11903 , 2023 .

WANG X Z , WEI J , SCHUURMANS D , et al . Self- consistency improves chain of thought reasoning in language models [EB/OL ] . arXiv: 2203.11171 , 2023 .

李舟军 , 范宇 , 吴贤杰 . 面向自然语言处理的预训练技术研究综述 [J ] . 计算机科学 , 2020 , 47 ( 03 ): 162 - 173 .

LI Zhoujun , FAN Yu , WU Xianjie . A Survey of Pre-trained Technology Research for Natural Language Processing [J ] . Computer Science , 2020 , 47 ( 03 ): 162 - 173 .

秦涛 , 杜尚恒 , 常元元 , 等 . ChatGPT的工作原理、关键技术及未来发展趋势 [J ] . 西安交通大学学报 , 2024 , 58 ( 01 ): 1 - 12 .

QIN Tao , DU Shangheng , CHANG Yuanyuan , et al . Working Principle, Key Technologies and Future Development Trends of ChatGPT [J ] . Journal of Xi'an Jiaotong University , 2024 , 58 ( 01 ): 1 - 12 .

任磊 , 王海腾 , 董家宝 , 等 . 工业大模型: 体系架构、关键技术与典型应用 [J ] . 中国科学:信息科学 , 2024 , 54 ( 11 ): 2606 - 2622 .

REN Lei , WANG Haiteng , DONG Jiabao , et al . Industrial Large Models: Architecture, Key Technologies and Typical Applications [J ] . Scientia Sinica Informationis , 2024 , 54 ( 11 ): 2606 - 2622 .

ZHONG D , XIA Z , ZHU Y , et al . Overview of predictive maintenance based on digital twin technology [J ] . Heliyon , 2023 , 9 ( 4 ): e14534 .

林金山 , 李元 , 方荠萱 , 等 . 电力变压器故障诊断多智能体大模型构建与应用 [J ] . 西安交通大学学报 , 2025 , 59 ( 10 ): 22 - 31 .

LIN Jinshan , LI Yuan , FANG Qixuan , et al . Large Multi-Agent Model for Power Transformer Fault Diagnostic and Preliminary Application [J ] . Journal of Xi'an Jiaotong University , 2025 , 59 ( 10 ): 22 - 31 .

江秀臣 , 臧奕茗 , 谢子恒 , 等 . 类DeepSeek范式在电力装备故障诊断中的应用模式 [J ] . 高电压技术 , 2025 , 51 ( 08 ): 4006 - 4021 .

JIANG Xiuchen , ZANG Yiming , XIE Ziheng , et al . Application Mode of DeepSeek-like Paradigm in Fault Diagnosis of Power Equipment [J ] . High Voltage Engineering , 2025 , 51 ( 08 ): 4006 - 4021 .

张嘉鑫 , 王波 , 董旭柱 , 等 . 面向新型配电系统的通信模型互译大模型Trans-DeepSeek [J/OL ] . 电力系统自动化 , 1 - 16 [ 2026-05-08 ] .

ZHANG Jiaxin , WANG Bo , DONG Xuzhu , et al . Trans-DeepSeek: A Large Model for Mutual Translation of Communication Models Oriented to New Distribution Systems [J/OL ] . Automation of Electric Power Systems , 1 - 16 [ 2026-05-08 ] .

PARIKH P P , SIDHU T S , SHAMI A . A comprehensive investigation of wireless LAN for IEC 61850–based smart distribution substation applications [J ] . IEEE Transactions on Industrial Informatics , 2012 , 9 ( 3 ): 1466 - 1476 .

YAO Q , FANG F , CHEN Y , et al . AI Large Models for Power System: A Survey and Outlook [J ] . IET Smart Energy Systems , 2025 , 1 ( 01 ): 3 - 21 .

LUO H , LIU Y , ZHANG R , et al . Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration [J ] . arXiv: 2507.00672 , 2025 .

YUAN X , HUANG Q , GUO M , et al . Towards Next-Generation Intelligent Maintenance: Collaborative Fusion of Large and Small Models [J ] . arXiv: 2506.05854 , 2025 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Construction and Application of Multi-Agent Large Language Model for Power Transformer Fault Diagnosis

Numerical Simulation of the Energy Conversion Process in Thermoacoustic Engines with Different Stack Materials

Effects of Expansion Angle Crater Film Hole on the Film Cooling Characteristics of a Double-Wall Structure

A Fast Calculation Method for Thermal Flow Fields in Transformer Windings Based on PI-DeepONet

Review of the Application of EBSILON Software in Combined Heat and Power System Optimization

Related Author

LIN Jinshan

LI Yuan

FANG Qixuan

LIU Zhihao

LI Chunpeng

ZHANG Guanjun

Related Institution

西安交通大学电气工程学院，710049，西安School of Electrical Engineering, Xi'an Jiaotong University, Xi'an 710049, China

⁰