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

LIN Jinshan; LI Yuan; FANG Qixuan; LIU Zhihao; LI Chunpeng; ZHANG Guanjun

doi:10.7652/xjtuxb202510002

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

更新时间：2025-09-28

- Construction and Application of Multi-Agent Large Language Model for Power Transformer Fault Diagnosis
  增强出版
- Journal of Xi'an Jiaotong University Vol. 59, Issue 10, Pages: 22-31(2025)
- 作者机构：
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- DOI：10.7652/xjtuxb202510002
  CLC： TM83
- Received：06 March 2025，
  
  Published：10 October 2025
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LIN Jinshan, LI Yuan, FANG Qixuan, et al. Construction and Application of Multi-Agent Large Language Model for Power Transformer Fault Diagnosis[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 22-31.
DOI：

LIN Jinshan, LI Yuan, FANG Qixuan, et al. Construction and Application of Multi-Agent Large Language Model for Power Transformer Fault Diagnosis[J]. Journal of Xi'an Jiaotong University, 2025, 59(10): 22-31. DOI： 10.7652/xjtuxb202510002.

摘要

针对电力变压器状态参量多元异构、数据缺失导致的传统小模型适用性受限、诊断效果不佳等问题，提出了一种电力变压器故障诊断多智能体大模型框架。该框架构建了由故障案例知识图谱和3个智能体协同的推理体系。故障案例知识图谱通过约束机制增强大模型对电力专业知识的理解能力，并有效抑制机器幻觉现象。3个智能体模拟专家诊断过程，将复杂的变压器故障诊断任务分解执行：初级诊断智能体通过特征阈值分析实现故障初步识别；专家诊断智能体利用概率图模型对典型故障模式进行不确定性推理；案例分析智能体接入历史故障案例库，完成知识回溯与诊断结果验证。验证结果表明：所提模型对100例故障案例诊断表现优异，诊断准确率可达86%，相比BERT模型提高了33%；故障案例知识图谱的引入提升了大模型输出信息的完整性、语义一致性和专业深度，专家评分平均增幅为50%。相较于单一的大模型，该智能体大模型在降低误判率方面表现突出，可为电力变压器的故障智能诊断与运维提供技术支撑。

Abstract

To address the limited applicability of traditional small language models and the unsatisfactory diagnostic effects caused by the multi-dimensional heterogeneity of state parameters and data loss in power transformers

a multi-agent large language model framework for transformer fault diagnosis is proposed. The framework establishes a collaborative reasoning system with fault cases knowledge graph and three agents. The fault cases knowledge graph constraint mechanism significantly enhances the large language model's understanding of power engineering expertise while effectively mitigating machine hallucination. Three agents simulate expert diagnostic processes by decomposing complex transformer fault diagnosis tasks. Primary diagnostic agents perform preliminary fault identification through feature threshold analysis

expert diagnostic agents conduct uncertainty reasoning on typical fault patterns using probabilistic graphical models

and case analysis agents access a historical fault case database to enable knowledge retrieval and diagnostic result validation. Validation results show that the proposed model achieves excellent performance in diagnosing 100 fault cases

with an accuracy rate of 86%

representing a 33% improvement over the BERT model. The integration of the fault cases knowledge graph enhances the large language model's output in terms of completeness

semantic consistency

and professional depth

with an expert rating average performance increase of 50%.This multi-agent large language model demonstrates superior performance in reducing misjudgment rates compared to monolithic large language models

and can provide technical support for intelligent fault diagnosis and operation and maintenance of power transformers.

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references

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