首页 | 本学科首页   官方微博 | 高级检索  
     

基于贝叶斯网络的智能电表故障类型预测
引用本文:郑安刚,张密,曲明钰,赵兵,陈昊,熊秋. 基于贝叶斯网络的智能电表故障类型预测[J]. 电测与仪表, 2018, 55(21): 143-147
作者姓名:郑安刚  张密  曲明钰  赵兵  陈昊  熊秋
作者单位:中国电力科学研究院,中国电力科学研究院,北京,北京邮电大学,中国电力科学研究院,中国电力科学研究院,北京邮电大学
基金项目:用电信息采集运行维护及现场移动作业关键技术研究
摘    要:针对智能电能表受到外界各种因素影响出现的故障,本文提出了一种基于贝叶斯网络的智能电能表故障类型分类与预测模型。分析了造成智能表故障的各种因素和常见的故障类型,通过大量历史故障数据的训练,结合专家意见,采用了基于评分搜索的方法构建了贝叶斯网络结构,在此基础上进行了故障类型预测和决策分析,并对提出的方法进行验证。研究结果表明:该方法可以有效地对智能表的故障类型进行预测,计算效率高,具有较好的适用性。

关 键 词:贝叶斯网络  智能电能表  条件概率表  K2算法
收稿时间:2018-05-14
修稿时间:2018-05-14

The prediction of the fault type of smart meters based on the Bayesian Network
zheng angang,ZHANG Mi,qu mingyu,zhao bing,chen hao and xiong qiu. The prediction of the fault type of smart meters based on the Bayesian Network[J]. Electrical Measurement & Instrumentation, 2018, 55(21): 143-147
Authors:zheng angang  ZHANG Mi  qu mingyu  zhao bing  chen hao  xiong qiu
Affiliation:China Electric Power Research Institute,China Electric Power Research Institute,Beijing University of Posts and Telecommunications,China Electric Power Research Institute,China Electric Power Research Institute,Beijing University of Posts and Telecommunications
Abstract:For the failure of smart meter due to various external factors,this paper presents a classification and prediction model of smart meter fault type based on Bayesian networks. First of all, we analyzed the various factors that caused the failure of smart meter,and then training model with a large number of historical failure data, combined with expert advice, the Bayesian network structure is constructed based on scoring, The fault type prediction and decision analysis are carried out, and theSperformance ofStheSproposedSmethodisSverifiedS. The results show that this method can effectively predict the fault type of smart meter, and has high computational efficiency and good applicability.
Keywords:Bayesian  network, Smart  meter, CPT, K2 algorithm
点击此处可从《电测与仪表》浏览原始摘要信息
点击此处可从《电测与仪表》下载免费的PDF全文
设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号