Open access
Aug 2026
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.
Rupali Balabantaraya, A. Chatterjee, A. Sahoo et al.
· Electrica · 0 citations