Enhancing gas formation theory assessment in power transformers by using decision tree transparency and new guess into decomposition temperatures of insulating mineral oil

被引：0

作者：

Mateus M. Araujo

Otacilio M. Almeida

Fabio R. Barbosa

Abraão G. C. Menezes

机构：

[1] Equatorial Electricity Utility Company,Department of Technical and Commercial Services

[2] Federal University of Piaui,Department of Electrical Engineering

[3] Equatorial Electricity Utility Company,Department of Operational Studies

来源：

Neural Computing and Applications | 2024年 / 36卷

关键词：

Decision tree; Dissolved gas analysis (DGA); Fault diagnosis;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

The analysis of gases dissolved in oil is the main technique used to predict possible failures in power transformers. Currently, there are some assessment algorithms for the predictive analysis, but all of them have in common the fact that they are based on the classical gas formation theory. This work aims to analyze the use of decision trees (DTs) for the analysis of gases dissolved in mineral oil, also establishing a relationship associated with decision making during the formation of trees and the formation of gases. In this way, some important aspects involving the insulating mineral oil can be better understood and quantified. For this purpose, one dataset containing 201 samples is used in the study. The results provided by the tree are arranged in a Cartesian plane, and the gases are analyzed based on the ratios used by Doernenburg. DT is discussed in detail to validate the algorithm performance based on the classical gas formation theory.

引用

页码：3259 / 3266

页数：7