2021
DOI: 10.3390/en14071809
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Transformer Oil Quality Assessment Using Random Forest with Feature Engineering

Abstract: Machine learning is widely used as a panacea in many engineering applications including the condition assessment of power transformers. Most statistics attribute the main cause of transformer failure to insulation degradation. Thus, a new, simple, and effective machine-learning approach was proposed to monitor the condition of transformer oils based on some aging indicators. The proposed approach was used to compare the performance of two machine-learning classifiers: J48 decision tree and random forest. The s… Show more

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Cited by 21 publications
(2 citation statements)
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“…On the other hand, international researchers in the field of power transformer defect warning have focused a lot on data-driven models, such as artificial neural networks, support vector machines, random forests, and principal component analysis, to solve these problems better [10][11][12][13]. In [14], a training technique for deriving rules from a functionally approximated ANN utilizing the concentration of dissolved gases in transformer oil as the input is suggested in order to implement fault warning and defect diagnostics in transformers using artificial neural networks.…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, international researchers in the field of power transformer defect warning have focused a lot on data-driven models, such as artificial neural networks, support vector machines, random forests, and principal component analysis, to solve these problems better [10][11][12][13]. In [14], a training technique for deriving rules from a functionally approximated ANN utilizing the concentration of dissolved gases in transformer oil as the input is suggested in order to implement fault warning and defect diagnostics in transformers using artificial neural networks.…”
Section: Introductionmentioning
confidence: 99%
“…The proposed ML regression models (ExtraTress, KNN, and XGBoost) have been proven to be effective in assessing the quality of transformer oils in numerous studies [17,18,24]. These algorithms are successful in many fields, such as image processing, biomedicine, and data science.…”
Section: Introductionmentioning
confidence: 99%