  An intrinsic time-scale decomposition (ITD) based method for power
transformer fault diagnosis is proposed. Dissolved gas analysis (DGA)
parameters are ranked according to their skewness, and then ITD based features
extraction is performed. An optimal set of PRC features are determined by an
XGBoost classifier. For classification purpose, an XGBoost classifier is used
to the optimal PRC features set. The proposed method's performance in
classification is studied using publicly available DGA data of 376 power
transformers and employing an XGBoost classifier. The Proposed method achieves
more than 95% accuracy and high sensitivity and F1-score, better than
conventional methods and some recent machine learning-based fault diagnosis
approaches. Moreover, it gives better Cohen Kappa and F1-score as compared to
the recently introduced EMD-based hierarchical technique for fault diagnosis in
power transformers.
