An EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier

TL;DR

This paper introduces an Empirical Mode Decomposition-based approach combined with a hierarchical ensemble classifier to accurately detect transformer faults from DGA data, achieving over 90% sensitivity and accuracy.

Contribution

The paper presents a novel EMD-based feature extraction method coupled with a hierarchical XGBoost classifier for transformer fault detection, outperforming existing techniques.

Findings

Achieved over 90% sensitivity and accuracy in fault detection.

Demonstrated superior performance compared to conventional and existing machine learning methods.

Validated on publicly available DGA data of 377 transformers.

Abstract

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.