Studying Intelligent Techniques Acting in Large Power Transformer Monitoring

IF 1 4区 生物学 Q3 BIOLOGY Brazilian Archives of Biology and Technology Pub Date : 2023-07-03 DOI:10.1590/1678-4324-2023220556
Elvis Ricardo de Oliveira, Vanias de Araujo Junior, José Faustino da Silva Cândido, G. Lambert-Torres, Luiz Eduardo Borges da Silva, E. Bonaldi, G. C. C. D. Andrade, Levy Ely de Lacerda de Oliveira, C. H. V. Moraes, Carlos Eduardo Teixeira
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Abstract

: The presented development is an intelligent diagnostic system for transformers that studied machine learning techniques to determine the operational status of these transformers. The study of these techniques is initiated by observing the quantities that define the operational behavior of large transformers, aiming to identify anomalies in their operation from data from sensors that equipment it in the functioning environment. This large power transformer has a theoretical service life of above 20 years and a low failure rate. Thus, obtaining failure values, which have their evolution monitored for large transformers, is almost nil. Therefore, a supervised machine training methodology to diagnose these cases is practically unfeasible. The study carried out with several traditional intelligent techniques can verify this. Several supervised methods (Closest Neighbor K-th Neighbor, Support Vector Machine, Radial Base Function, Decision Trees, Random Forest, Neural Network, AdaBoost, Gaussian Naive Bayes, and Quadratic Discriminant Analysis) were studied.
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智能技术在大型电力变压器监测中的应用研究
提出了一种变压器智能诊断系统,该系统研究了机器学习技术来确定这些变压器的运行状态。这些技术的研究是通过观察定义大型变压器运行行为的数量来启动的,旨在通过在功能环境中安装变压器的传感器的数据来识别其运行中的异常。这种大型电力变压器理论使用寿命在20年以上,故障率低。因此,对大型变压器监测其演变过程的失效值的获取几乎为零。因此,一个有监督的机器训练方法来诊断这些病例实际上是不可行的。利用几种传统智能技术进行的研究可以验证这一点。研究了几种监督方法(最近邻k邻、支持向量机、径向基函数、决策树、随机森林、神经网络、AdaBoost、高斯朴素贝叶斯和二次判别分析)。
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