Fault Prediction and Classification in Power Distribution Transformer Using Machine Learning

Zawar Ahmed Khan; Muhammad Amir Raza; Ramesh Kumar; Shakir Ali Soomro; Hizbullah Odho

doi:10.30537/sjet.v8i2.1585

Authors

Zawar Ahmed Khan Department of Electrical Engineering Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs’ Sindh Pakistan
Muhammad Amir Raza Department of Electrical Engineering Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs’ Sindh Pakistan
Ramesh Kumar Senior Manager (Power Distribution) K-Electric Karachi, Sindh Pakistan
Shakir Ali Soomro Department of Electrical Engineering Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs’ Sindh Pakistan
Hizbullah Odho Department of Electrical Engineering Mehran University of Engineering and Technology, SZAB Campus Khairpur Mirs’ Sindh Pakistan

DOI:

https://doi.org/10.30537/sjet.v8i2.1585

Keywords:

Power transformer, fault detection, machine learning, sustainability

Abstract

This article used Random forest (RF) classifier, decision tree (DT) classifier, K-nearest neighbors (KNN) classifiers, extra tree (ET) classifier and extreme gradient boosting (XGB) classifier for fault prediction and classification in power distribution transformer to predict the phase line voltages, line currents, line-to-line voltages, neutral current, ambient temperature indicator, oil temperature indicator, oil level indicator, and various other alarm indicators like oil temperature indicator trip oil temperature indicator alarm and magnetic oil guage alarm. The findings demonstrate that XGB and ET classifier, provide superior predictive capabilities compared to other methods.

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