New Dissolved Gas Analysis Diagnostics Framework for Substation Transformers using Random Forest Algorithm and IEEE C57.104 – 2019TM Guide

Dissolved Gas Analysis (DGA) is a practical, non-intrusive test to check transformer health status, as it is widely used in the field. However, the traditional methods of DGAbased diagnostics have intrinsic weaknesses. For example, the Rogers ratio method is limited only to gases involved in the computation. The interpretation of the IEC Ratio method can be unknown at some point. The Duval triangle method cannot diagnose healthy degradation of oil from faulty ones. All traditional methods were subject to expert subjective judgment. To fill these gaps, this paper introduces the two-layer framework using a random forest algorithm with the IEEE C57.104 – 2019TM guide as a watchdog (layer 1) for unhealthy oil degradation versus normal ones. The prediction model (layer 2) used the random forest algorithm. Using the 277 DGA datasets from Distribution Utilities from different parts of the Philippines, the framework surpassed the accuracy of traditional methods (Duval triangle method, IEC ratio, Doernunberg method) with an accuracy of 100%. The Duval triangle got 98.92% accuracy, the IEC ratio had 28.32% accuracy, and the Doernunberg method had an accuracy of 27.50%. Other ML algorithms, such as ANN (MLP), K-nearest neighbors, SVM (linear), and J48, were also used for comparison. The ANN (MLP), K-Nearest neighbor, and SVM (linear) got 78.6%, 85.7%, and 78.6% accuracy, respectively. The random forest got the highest cross-validation score (89.14% ave.) among all ML methods. Further evaluations were used for J48, DT, and Random Forest since all got 100% accuracy. RF algorithm still got the highest PR-AUC (94%, 89%) and ROCAUC (95%, 97%) scores among the J48 and DT in the 70/30 and 80/20 data split.