基于主元分析的关联规则挖掘及在电厂中的应用  

Association Rules Mining Based on Principal Component Analysis and Its Applications in Power Plant

作　　者：邱凤翔[1] 徐治皋[1] 司风琪[1] 赵跃[1] 

机构地区：[1]东南大学能源与环境学院,江苏省南京市210096

出　　处：《电力系统自动化》2009年第16期81-84,107,共5页Automation of Electric Power Systems

摘　　要：针对目前火电厂厂级监控信息系统(SIS)的海量数据没有得到充分开发和利用的现状,提出了基于主元分析(PCA)的相似关联规则数据挖掘方法,并讨论了其在电厂中参数相关性分析和异常数据检验等方面的应用。通过.NET平台,读取火电厂SIS中实时数据库PI的历史数据,然后将数据在MATLAB环境下进行主元分析,寻找相似关联测点。通过对某600MW机组数据进行挖掘,表明了该挖掘方法能针对某具体机组运行状况,准确快速地寻找具有较高相似关联规则的参数,从而利用这些关联参数进行多方面的实际应用,并取得很好的效果。In view of the insufficient exploitation of the massive data from the thermal power plant supervisory information system （SIS）, a similarity association rules data mining method based on principal component analysis （PCA） is proposed. Its applications in power plant parameter correlation analysis and abnormal data detection are discussed. Historical data from realtime database plant information （PI） of SIS through the . NET platform are analyzed by PCA in the MATLAB environment to find the similarity association measuring points. Data from a 600 MW unit show that the method proposed is capable of accurately and quickly seeking parameters with fairly high similarity association rules according to the operation of a specific unit, thus successfully utilizing these similarity association measuring points in different ways.

关 键 词：主元分析 关联规则 相关性分析 数据检测 火电厂 

分 类 号：TM621.6[电气工程—电力系统及自动化]