Work

Fault Prediction and Classification in Power Distribution Transformer Using Machine Learning

Year: 2025

Type: article

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.

Source: Sukkur IBA Journal of Emerging Technologies

DOI: 10.30537/sjet.v8i2.1585

Authors: Zawar Ahmed Khan, Muhammad Amir Raza, Ramesh Kumar, Shakir Ali Soomro, Hizbullah Odho

Institutions: Mehran University of Engineering and Technology, Mehran University of Engineering & Technology Shaheed Zulfiqar Ali Bhutto Campus, Khairpur Mir's

Language: English

Topic: Power Transformer Diagnostics and Insulation

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: diamond

Funders: University of Engineering and Technology, Lahore, Mehran University of Engineering and Technology