Transformer Fault Classification Based on Dissolved Gas Compositions Using Microsoft Azure Machine Learning
This study presents a novel diagnostic methodology for oil-filled transformers, leveraging dissolved gas analysis (DGA) and a no-code machine learning framework. Conventional diagnostic methods such as Duval Triangle and gas ratio techniques are constrained by limited adaptability to modern transformer designs, susceptibility to misdiagnosis, and reliance on expert interpretation. Meanwhile, artificial intelligence (AI) approaches often demand significant programming expertise, large datasets, and computational resources. To address these challenges, the research introduced a unified machine learning model developed using Microsoft Azure Machine Learning, enabling field engineers to build and deploy diagnostic models without coding. The model utilized concentrations of 5 key gases: methane, ethylene, acetylene, ethane, and hydrogen, as input features to classify transformer conditions into 6 fault classes, including a no-fault state. 3 algorithms, namely Gradient Boosting, Random Forest, and Logistic Regression were evaluated, with Gradient Boosting consistently outperforming other models and Duval Triangle, achieving the highest accuracy of 81.3% with 2 false negatives. When the output labels were consolidated to 4 fault types, diagnostic accuracy improved to 95.6% without any false negative. Model performance was optimized through experiments involving train-test ratio variation and feature scaling. The model was further validated through field trials on actual transformers, demonstrating high reliability and alignment with forensic findings. This no-code approach empowers engineers to integrate domain expertise directly into model development, enhancing diagnostic precision and operational efficiency. The findings support scalable, interpretable, and proactive maintenance strategies, contributing to resilient infrastructure and sustainable energy systems.