电测与仪表2017，Vol.54Issue(4)：50-56,7.
基于正态云模型与改进贝叶斯分类器的变压器故障诊断
Transformer fault diagnosis based on normal cloud model and improved Bayesian classifier
张重远 1林志锋 2刘栋 2黄景立3
作者信息
- 1. 华北电力大学 高电压研究所,河北 保定 071003
- 2. 华北电力大学 电气与电子学院,河北 保定 071003
- 3. 国网山西省电力公司计量中心,太原 030032
- 折叠
摘要
Abstract
In order to solve the problem of data mining in diagnosing transformer fault based on oil chromatographic data,that data of dissolved gas-in-oil is divided without considering the randomness and fuzziness,so the normal cloud model is applied.The efficiency of mining association rules is also improved through normal cloud model.For the assumption in Naive Bayes classifier is not conformed to the actual situation,an association rule forest and a method of the joint probability calculated are applied to improve Naive Bayes classifier,and the transformer fault diagnosis model based on normal cloud model and improved Bayesian classifier is built as a result.The new Bayes classifier is proved to be practical in the diagnosis of transformer by comparing with other classifier and testing examples.关键词
数据挖掘/变压器/故障诊断/云模型/贝叶斯分类器Key words
data mining/transformer/fault diagnosis/cloud model/Bayes classifier分类
信息技术与安全科学引用本文复制引用
张重远,林志锋,刘栋,黄景立..基于正态云模型与改进贝叶斯分类器的变压器故障诊断[J].电测与仪表,2017,54(4):50-56,7.
