采用知识增强大语言模型的输电线路故障处置智能辅助决策方法

周良才; 刘林林; 孙志豪; 史迪; 陈欣

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采用知识增强大语言模型的输电线路故障处置智能辅助决策方法

更新时间：2026-09-04

- 采用知识增强大语言模型的输电线路故障处置智能辅助决策方法
- Knowledge-Augmented Large Language Model-Based Intelligent Auxiliary Decision-Making for Transmission-Line Fault Handling
- 西安交通大学学报 2026年
- 作者机构：
  
  1.国家电网有限公司华东分部，200120，上海
  2.智博能源科技（江苏）有限公司，211300，南京
  3.西安交通大学电气工程学院，710049，西安
- 作者简介：
- 基金信息：
- DOI：
  中图分类号： TM771
- 收稿：2026-07-11，
  
  修回：2026-08-24，
  
  录用：2026-09-04，
- 稿件说明：
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周良才, 刘林林, 孙志豪, 等. 采用知识增强大语言模型的输电线路故障处置智能辅助决策方法[J]. 西安交通大学学报,2026.

ZHOU Liangcai, LIU Linlin, SUN Zhihao, et al. Knowledge-Augmented Large Language Model-Based Intelligent Auxiliary Decision-Making for Transmission-Line Fault Handling[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026.
周良才, 刘林林, 孙志豪, 等. 采用知识增强大语言模型的输电线路故障处置智能辅助决策方法[J]. 西安交通大学学报,2026. DOI：

ZHOU Liangcai, LIU Linlin, SUN Zhihao, et al. Knowledge-Augmented Large Language Model-Based Intelligent Auxiliary Decision-Making for Transmission-Line Fault Handling[J]. JOURNAL OF XI’AN JIAOTONG UNIVERSITY,2026. DOI：

摘要

针对输电线路故障处置中规程匹配效率低、历史案例关联不足，以及通用大语言模型缺乏电气机理约束、易生成不可靠建议的问题，提出一种知识增强大语言模型智能辅助决策方法。该方法在检索增强生成框架下，构建融合序分量特征、告警事件轨迹、故障处置规程和典型案例的跨模态知识库，设计物理一致性约束的混合检索评分函数，将语义相似度、电气特征相似度、元数据匹配度和故障机理一致性纳入统一相关性度量，并引入置信度感知生成机制，在证据不足或证据冲突时触发保守提示或人工复核。基于多场景输电线路故障问答样本和IEEE 39 节点系统进行验证，结果表明：Top-1和Top-3 检索命中率、故障诊断准确率、处置完整性和依据可追溯率分别达到88.6%、95.7%、94.3%、91.4% 和 94.3%，幻觉率降至2.9%；相较仅语义RAG方法，故障诊断准确率提高11.4%，幻觉率降低8.5%。该方法通过显式融合故障物理机理、规程知识和大语言模型推理能力，提高了输电线路故障处置建议的准确性、可追溯性和安全可靠性。

Abstract

To address inefficient rule matching

insufficient historical-case association

and unreliable suggestions from general large language models in transmission-line fault handling

this paper proposes a knowledge-enhanced LLM-based intelligent decision-support method. Within a retrieval-augmented generation framework

the method constructs a cross-modal knowledge base integrating sequence-component features

alarm-event trajectories

fault-handling procedures

and typical cases. A physics-consistency-constrained hybrid retrieval score is developed to jointly measure semantic similarity

electrical-feature similarity

metadata matching

and fault-mechanism consistency. A confidence-aware generation mechanism is also introduced to trigger conservative prompts or manual review when evidence is insufficient or conflicting. Validation using multi-scenario transmission-line fault question-answering samples and the IEEE 39-bus system shows that the Top-1 and Top-3 retrieval hit rates

fault-diagnosis accuracy

decision completeness

and evidence traceability reach 88.6%

95.7%

94.3%

91.4%

and 94.3%

respectively

while the hallucination rate is reduced to 2.9%. Compared with semantic-only RAG

the method improves fault-diagnosis accuracy by 11.4% and reduces the hallucination rate by 8.5%. The results demonstrate that explicitly integrating fault mechanisms

procedural knowledge

and LLM reasoning improves the accuracy

traceability

and safety reliability of transmission-line fault-handling suggestions.

关键词

Keywords

references

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