Transformer Fault Detection with Vibration using ML

Author(s)	Prof. Vineeth V.V, Mr. Rejiv Elshan Nify J, Ms. Rathika P, Mr. Sharath K, Mr. Suriya Prasath NS
Country	India
Abstract	The project focuses on transformer fault detection by integrating vibration, current, and voltage sensors. Utilizing machine learning techniques, specifically K-Nearest Neighbors (KNN) and Random Forest algorithms, the system aims to identify and classify various faults such as short circuit, overvoltage, undervoltage, and high vibration. The sensors provide real-time data, which is used to train the models for accurate fault prediction. KNN leverages proximity-based classification, while Random Forest utilizes an ensemble of decision trees to enhance accuracy. The trained models enable quick and precise identification of transformer faults, contributing to early detection and prevention of potential damage. This integrated approach harnesses the power of machine learning to improve the reliability and efficiency of transformer systems in power distribution networks.
Keywords	Transformer Fault Detection, Machine Learning Analysis, Edge Computing, Real-Time Monitoring, Proactive Maintenance
Field	Engineering
Published In	Volume 16, Issue 3, July-September 2025
Published On	2025-07-05
DOI	https://doi.org/10.71097/IJSAT.v16.i3.6781
Short DOI	https://doi.org/g9sx6z

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