Comparison of Fault Identification Methods for Analyzing Transformer Dissolved Gas*

This study provides a thorough comparison of defect detection methods used in dissolved gas analysis (DGA) of transformers. The three widely used methods-Roger's Ratio, Doernenburg Ratio, and Artificial Neural Network (ANN)-are the subject of the study. As part of the process, each technique's codes are represented, their MATLAB program implementations are explained, and crucial gas ratios are used to detect early defects. Using two different datasets, the research analyzes their effectiveness and accuracy in diagnosing various defect kinds. For various fault scenarios, the effectiveness of these techniques is graphically depicted and examined. The study discusses how these techniques can improve the accuracy of transformer fault diagnostics.