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

Author

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  • Ahmad Nayyar Hassan

    (Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Ayman El-Hag

    (Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

Abstract

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.

Suggested Citation

  • Ahmad Nayyar Hassan & Ayman El-Hag, 2020. "Two-Layer Ensemble-Based Soft Voting Classifier for Transformer Oil Interfacial Tension Prediction," Energies, MDPI, vol. 13(7), pages 1-11, April.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1735-:d:341697
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    References listed on IDEAS

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    1. Alhaytham Alqudsi & Ayman El-Hag, 2019. "Application of Machine Learning in Transformer Health Index Prediction," Energies, MDPI, vol. 12(14), pages 1-13, July.
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    Cited by:

    1. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
    2. Denis Sidorov & Fang Liu & Yonghui Sun, 2020. "Machine Learning for Energy Systems," Energies, MDPI, vol. 13(18), pages 1-6, September.
    3. Jesús de-Prado-Gil & Osama Zaid & Covadonga Palencia & Rebeca Martínez-García, 2022. "Prediction of Splitting Tensile Strength of Self-Compacting Recycled Aggregate Concrete Using Novel Deep Learning Methods," Mathematics, MDPI, vol. 10(13), pages 1-21, June.

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