An EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier
This work addresses fault detection for power transformers, which is critical for maintenance in electrical grids, but it appears incremental as it builds on existing DGA and machine learning techniques.
The paper tackled the problem of detecting power transformer faults from Dissolve Gas Analysis (DGA) data by proposing an Empirical Mode Decomposition-based method with a hierarchical ensemble classifier, achieving over 90% sensitivity and accuracy on a dataset of 377 transformers.
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.