Rupali Balabantaraya

A Machine Learning Framework for Improved Fault Diagnosis in Service Transformers Using Dissolved Gas Analysis

This article explores machine learning techniques (MLTs) as a modern alternative to enhance the interpretation of DGA data for early-stage fault detection in service transformers, and demonstrates that random forest and gradient boosting outperform others, achieving up to 98% accuracy.