DGA results

Power transformers are critical components in a power system network and they are subjected to multifactorial stresses. The dissolved gas composition of an oil-filled transformer provides good indication of the transformer health. Numerous methods have been developed to interpret the gas data, both conventional and artificial intelligent based. There are issues with inaccurate diagnosis, high dependency on expert judgment and rigid diagnosis standards as transformer designs, materials and issues continue to evolve. This paper attempts to predict transformer fault using Multiclass Classification Models by Microsoft Azure ML Studio, based on Duval Triangle 1. Trained with 628 samples and validated with 269 samples, the One-vs-All Multiclass model achieved an overall accuracy of 90.3%. The model is then compared with 4 published works of similar nature. The output fault classes of this work are clustered accordingly to facilitate good comparison. Its performance in terms of prediction accuracy is superior when compared with SOM (73.6%) and SVM (78.5%) models, similar as Auto-WEKA Random Forest model (90.3%) and inferior when compared with ANFIS (97.7%). When validated with 122 datasets of inspected and diagnosed faulty transformers from IEC TC 10 database and published works, the overall accuracy is 76.2% for 7 output fault classes. The prediction accuracy advances to 91.8% when the fault classes are clustered into 4 fault types. Less output classes result in higher accuracy as there is lower probability for the diagnosis to fall into adjacent or neighboring classes. More training data is expected to improve the overall performance of the model.