摘要
为提升电力系统二次设备的运维和管控水平,从二次设备的缺陷数据出发,提出了基于Apriori算法的二次设备缺陷数据挖掘与分析方法。首先,分析了关联规则与Apriori算法的基本思路,然后建立了基于关联规则的二次设备缺陷模型,在模型中考虑了二次设备缺陷的几个重要属性:二次设备的生产厂家、设备类型、设备缺陷的原因、发生缺陷的设备部位以及缺陷等级。进一步,以一组自动化设备缺陷数据为例,阐述了基于Apriori算法的二次设备缺陷数据挖掘和分析方法,分析结果表明所提方法能够用于寻找二次设备的薄弱环节,并能够找到诱发薄弱环节的原因,同时还具有分析设备家族性缺陷等功能。
To enhance the maintenance and management level of secondary devices in the power system,a data mining and analysing method for secondary device defects based on the Apriori algorithm is proposed. Firstly,the basic ideas of association rules and Apriori algorithm are analyzed. Then a secondary defect model based on association rules is proposed,in which several important properties of secondary equipment defects（ including secondary equipment manufacturer,device type,causes of device defects,position of device defect and defect levels） are taken into account. Furthermore,by taking the defect data of automation equipment as examples,the defect data mining and analyzing method based on data mining results are presented. Analysis results show that the proposed method is able to search for the weaknesses of secondary devices and the causes of weaknesses,while enunciating the family defects of devices.
出处
《电力系统自动化》
EI
CSCD
北大核心
2017年第19期147-151,163,共6页
Automation of Electric Power Systems
