电力杆塔部件故障概率评估的知识图谱方法  

Method for Evaluating Fault Probability of Power Transmission Tower Components Based on Knowledge Graph

作　　者：魏敏 林世忠 尚文迪 WEI Min;LIN Shizhong;SHANG Wendi(State Grid Anhui Electric Power Co.,Ltd.,Ma′anshan Electric Power Supply Company,Ma′anshan 243000,China;Anhui Electric Power Transmission and Transformation Co.,Ltd.,Hefei 230000,China)

机构地区：[1]国网安徽省电力有限公司马鞍山供电公司,安徽马鞍山243000 [2]安徽送变电工程有限公司,安徽合肥230000

出　　处：《电工技术》2025年第2期160-166,共7页Electric Engineering

基　　金：国网安徽省电力有限公司科技项目“知识引导的无人机杆塔故障应急巡检航线智能生成技术研究及应用”(编号5212B023001)。

摘　　要：以1000 kV淮芜II线异物短路跳闸故障分析报告为例,提出了一种基于领域知识图谱构建的电力杆塔部件故障概率评估方法。通过整合大型语言模型以融合专家经验,运用TextRank算法处理故障语料,并基于Neo4j图数据库构建了故障巡检知识图谱,在此基础上,通过拟合多个杆塔多个部件故障概率的分布情况,该图谱可以快速准确地定位多级杆塔上的故障部件和计算故障概率。该方法为无人机应急巡检航线规划提供了数据支持,展现出良好的实际应用价值。Taking a foreign object short-circuit fault analysis report on the 1000 kV Huaiwu II line as an example,this paper proposes a method for evaluating the probability of electrical tower component faults based on the construction of a domain knowledge graph.This method integrates large-scale language models to amalgamate expert experiences,utilizes the TextRank algorithm to process fault-related data,and establishes a fault inspection knowledge graph based on the Neo4j graph database.Building upon this,it models the distribution of faults across multiple components in multiple towers,facilitating the rapid and accurate identification and localization of faulty components across multi-level towers.This method provides data support for efficient and rational route planning for drone inspections and underscores its considerable practical value.

关 键 词：故障应急巡检 领域知识图谱 Neo4j图数据库 TextRank算法 

分 类 号：TP36[自动化与计算机技术—计算机系统结构]