  In this paper, an Empirical Mode Decomposition-based method is proposed for
the detection of transformer faults from Dissolve gas analysis (DGA) data.
Ratio-based DGA parameters are ranked using their skewness. Optimal sets of
intrinsic mode function coefficients are obtained from the ranked DGA
parameters. A Hierarchical classification scheme employing XGBoost is presented
for classifying the features to identify six different categories of
transformer faults. Performance of the Proposed Method is studied for publicly
available DGA data of 377 transformers. It is shown that the proposed method
can yield more than 90% sensitivity and accuracy in the detection of
transformer faults, a superior performance as compared to conventional methods
as well as several existing machine learning-based techniques.
