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Application of Machine Learning in Transformer Health Index Prediction

1
Mechanical Engineering Department, École de technologie supérieure, Montréal, QC H3C 1K3, Canada
2
Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
*
Author to whom correspondence should be addressed.
Energies 2019, 12(14), 2694; https://doi.org/10.3390/en12142694
Received: 18 June 2019 / Revised: 10 July 2019 / Accepted: 12 July 2019 / Published: 14 July 2019
(This article belongs to the Special Issue High Voltage Engineering and Applications)
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Abstract

The presented paper aims to establish a strong basis for utilizing machine learning (ML) towards the prediction of the overall insulation health condition of medium voltage distribution transformers based on their oil test results. To validate the presented approach, the ML algorithms were tested on two databases of more than 1000 medium voltage transformer oil samples of ratings in the order of tens of MVA. The oil test results were acquired from in-service transformers (during oil sampling time) of two different utility companies in the gulf region. The illustrated procedure aimed to mimic a realistic scenario of how the utility would benefit from the use of different ML tools towards understanding the insulation health index of their transformers. This objective was achieved using two procedural steps. In the first step, three different data training and testing scenarios were used with several pattern recognition tools for classifying the transformer health condition based on the full set of input test features. In the second step, the same pattern recognition tools were used along with the three training/testing scenarios for a reduced number of test features. Also, a previously developed reduced model was the basis to reduce the needed number of tests for transformer health index calculations. It was found that reducing the number of tests did not influence the accuracy of the ML prediction models, which is considered as a significant advantage in terms of transformer asset management (TAM) cost reduction. View Full-Text
Keywords: feature selection; insulation health index; machine learning; oil/paper insulation; transformer asset management feature selection; insulation health index; machine learning; oil/paper insulation; transformer asset management
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Alqudsi, A.; El-Hag, A. Application of Machine Learning in Transformer Health Index Prediction. Energies 2019, 12, 2694.

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