DGA Ground Truth Datasets

The integration of artificial intelligence (AI) techniques in fault diagnosis of oil-filled transformers offers a promising approach to enhance the reliability and efficiency of power distribution networks. This paper presents a methodology for developing machine learning (ML) models using a no-code approach in Azure Machine Learning to diagnose transformer faults based on dissolved gas analysis (DGA). By leveraging multiclass classification algorithms, the study aims to accurately identify fault types such as partial discharges, thermal faults, low and high-energy discharges. The proposed method addresses challenges associated with conventional DGA interpretation methods, including incomplete ratio ranges and the absence of a fault-free area. The research includes a comprehensive evaluation of model performance through various preprocessing techniques, cross-validation and field trials, demonstrating the model's robustness and practical applicability.