  This paper proposes methods to identify 40 different types of internal faults
in an Indirect Symmetrical Phase Shift Transformer (ISPST). The ISPST was
modeled using Power System Computer Aided Design (PSCAD)/ Electromagnetic
Transients including DC (EMTDC). The internal faults were simulated by varying
the transformer tapping, backward and forward phase shifts, loading, and
percentage of winding faulted. Data for 960 cases of each type of fault was
recorded. A series of features were extracted for a, b, and c phases from time,
frequency, time-frequency, and information theory domains. The importance of
the extracted features was evaluated through univariate tests which helped to
reduce the number of features. The selected features were then used for
training five state-of-the-art machine learning classifiers. Extremely Random
Trees and Random Forest, the ensemble-based learners, achieved the accuracy of
98.76% and 97.54% respectively outperforming Multilayer Perceptron (96.13%),
Logistic Regression (93.54%), and Support Vector Machines (92.60%)
