摘要
油浸式变压器的DGA数据富含大量的变压器故障信息,深度剖析DGA数据与变压器的故障状况有利于实现油浸式变压器的故障诊断。然而,特征气体信息与变压器故障类型,故障程度间为复杂的非线性映射关系,给基于变压器油中溶解气体的变压器故障判断工作带来了困难。本文综述了从三比值法到专家系统、模糊理论、机器学习等智能诊断方法,简述了各方法的优点与不足之处,此外利用决策树较强的分类性能,提出了基于决策树的变压器故障诊断模型,实验结果表明,该方法较传统三比值法有一定的优势。最后,对未来的DGA数据智能算法分析研究提供一些思路,提升变压器故障诊断准确率。
The DGA data of oil-immersed transformer contains a lot of fault information,which can be excavated and analyzed to achieve fault diagnosis of oil-immersed transformer.However,between characteristic gas information and transformer fault type and degree,there is a complex nonlinear mapping relationship.Therefore,it is very difficult to judge the fault of transformer.This paper summarizes the traditional fault analysis methods,such as three ratio method and the existing intelligent diagnosis methods,such as expert system,fuzzy theory and machine learning,and analyzes the principles and shortcomings of each method.In addition,using the strong classification performance of decision tree,a transformer fault diagnosis model based on decision tree is proposed.The experimental results show that this method has certain advantages over the traditional three ratio method.Finally,some ideas are provided for the future research of DGA data intelligent algorithm to improve the accuracy of transformer fault diagnosis.
作者
张英
徐龙舞
王明伟
刘喆
张倩
潘云
ZHANG Ying;XU Longwu;PAN Yun;ZHANG Qian;WANG Mingwei;LIU Zhe(Electric Power Research Institute of Guizhou Power Grid Co.,Ltd.,Guiyang 550002,Guizhou,China;Guizhou University,Guiyang 550025,Guizhou,China;Kaili Power Supply Bureau of Guizhou Power Grid Co.,Ltd.,Kaili 556000,Guizhou,China)
出处
《电力大数据》
2021年第12期55-61,共7页
Power Systems and Big Data
关键词
油浸式变压器
DGA数据
三比值法
专家系统
模糊理论
机器学习
决策树
oil immersed transformer
DGA data
three ratio method
expert system
fuzzy theory
machine learning
decision tree
