  This paper proposes a Decision Tree (DT) based detection and classification
of internal faults in a power transformer. The faults are simulated in Power
System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC
(EMTDC) by varying the fault resistance, fault inception angle, and percentage
of winding under fault. A series of features are extracted from the
differential currents in phases a, b, and c belonging to the time, and
frequency domains. Out of these, three features are selected to distinguish the
internal faults from the magnetizing inrush and another three to classify
faults in the primary and secondary of the transformer. DT, Random Forest (RF),
and Gradient Boost (GB) classifiers are used to determine the fault types. The
results show that DT detects faults with 100\% accuracy and the GB classifier
performed the best among the three classifiers while classifying the internal
faults.
