电力变压器故障类型与关键状态量关联规则分析

传统电力变压器设备运维大多采用状态检修技术,但积累的状态监测和检测数据没有得到充分挖掘利用,造成信息资源的浪费。以故障特征量为前项,以故障类型为后项,设置最小支持度和最小置信度,运用Apriori数据挖掘经典算法挖掘出变压器故障和关键状态量之间的关联规则。基于关联规则挖掘原理,利用SPSS Modeler软件平台建立电力变压器故障关联规则挖掘模型进行分析,得出了故障诊断的具体流程,旨在采取关联规则挖掘的方法发现状态特征量和故障类别之间的内在联系,对故障进行判定。

Analysis of the Power Transformer Fault Types and Critical State Variables Association

The operation and maintenance of traditional transformer equipment mostly adopt condition-based maintenance technology. However, the accumulated condition monitoring and detection data have not been fully mined, which results in the waste of information resources. The association rules between transformer faults and fault state variables are mined by using classical Apriori algorithm, in which the least support degree and the least confidence degree are set up with the fault characteristic quantity as the front term and the fault type as the rear term. Based on the software platform of SPSS Modeler, the model of power transformer fault association rules mining is established, which concludes the specific process of fault diagnosis and aims to adopt association rule mining method to detect the interior connection between state characteristic quantity and fault faults and judge the faults.