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Open AccessArticle

Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction

Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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Energies 2020, 13(7), 1735; https://doi.org/10.3390/en13071735
Received: 16 March 2020 / Revised: 2 April 2020 / Accepted: 3 April 2020 / Published: 5 April 2020
(This article belongs to the Special Issue Machine Learning for Energy Systems)
This paper uses a two-layered soft voting-based ensemble model to predict the interfacial tension (IFT), as one of the transformer oil test parameters. The input feature vector is composed of acidity, water content, dissipation factor, color and breakdown voltage. To test the generalization of the model, the training data was obtained from one utility company and the testing data was obtained from another utility. The model results in an optimal accuracy of 0.87 and a F1-score of 0.89. Detailed studies were also carried out to find the conditions under which the model renders optimal results. View Full-Text
Keywords: Interfacial tension; machine learning; transformer oil parameters Interfacial tension; machine learning; transformer oil parameters
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MDPI and ACS Style

Nayyar Hassan, A.; El-Hag, A. Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction. Energies 2020, 13, 1735. https://doi.org/10.3390/en13071735

AMA Style

Nayyar Hassan A, El-Hag A. Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction. Energies. 2020; 13(7):1735. https://doi.org/10.3390/en13071735

Chicago/Turabian Style

Nayyar Hassan, Ahmad; El-Hag, Ayman. 2020. "Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction" Energies 13, no. 7: 1735. https://doi.org/10.3390/en13071735

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