电力设备智能运维大模型的技术挑战与发展路径

李元; 林金山; 边畅; 方荠萱; 刘志濠; 张冠军

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电力设备智能运维大模型的技术挑战与发展路径

更新时间：2026-06-23

- 电力设备智能运维大模型的技术挑战与发展路径
- Technical Challenges and Development Pathway of Intelligent Maintenance in Power Equipment Using Large Language Model
- 西安交通大学学报 2026年
- 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：
  中图分类号： TM83
- 收稿：2026-04-12，
  
  修回：2026-06-17，
  
  录用：2026-06-23，
- 稿件说明：
移动端阅览
李元, 林金山, 边畅, 等. 电力设备智能运维大模型的技术挑战与发展路径[J/OL]. 西安交通大学学报, 2026.

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.
李元, 林金山, 边畅, 等. 电力设备智能运维大模型的技术挑战与发展路径[J/OL]. 西安交通大学学报, 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

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