摘要
为了充分挖掘油中溶解气体分析(DGA)数据隐藏的故障特征信息,提出了一种基于相对重构贡献(rRBC)的变压器故障诊断新方法。该方法首先利用DGA数据建立主元分析(PCA)模型,基于故障重构的思想,计算样本各变量重构贡献率(RBC);考虑各变量重构贡献率之间的可比性,计算其相对重构贡献率并作为特征量,通过归一化处理来提取故障特征;然后,建立变压器分层故障诊断模型,利用灰关联熵(GRE)信息利用率高等优点,求出待诊模式与各标准模式的综合灰熵关联序,实现故障诊断。实例研究结果表明,所提出的相对重构贡献灰关联熵方法与重构贡献灰关联熵、灰关联熵方法相比,使特征样本集的可分性变大,提高了分类正确率。
In order to fully get the hidden fault feature information in the dissolved gas analysis（DGA）data,a new method of the transformer fault diagnosis based on relative reconstruction-based contribution（rRBC）is proposed.The reconstruction-based contribution（RBC）of variables are calculated based on the idea of fault reconstruction after establishing aprincipal component analysis（PCA）model of dissolved gas-in-oil.Then,considering the RBCs＇ comparability of each variable,the feature information of oil dissolved gas is extracted by calculating the relative RBCs and normalization.Finally,a hierarchical structure of transformer fault diagnosis model is built up,and due to the high information utilizing rate of the grey relation entropy（GRE）,the synthesis GRE incidence order of the diagnosing pattern with standard patterns is computed to realize the fault analysis.The experimental results show that,compared with the features extracted by RBC-GRE and GRE,the proposed rRBC method increases the separability of data set and the classification accuracy.
出处
《高电压技术》
EI
CAS
CSCD
北大核心
2012年第11期2964-2971,共8页
High Voltage Engineering
基金
国家自然科学基金重点项目(61134006)
国家"十二五"科技支撑计划(2012BAK09B04)~~
关键词
故障诊断
电力变压器
溶解气体分析(DGA)
主元分析(PCA)
相对重构贡献(rRBC)
灰关联
fault diagnosis
power transformer
dissolved gas analysis（DGA）
principal component analysis（PCA）
relative reconstruction-based contribution（rRBC）
grey relation entropy（GRE）
