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

@article{Sami2020AnEM,
  title={An EMD-based Method for the Detection of Power Transformer Faults with a Hierarchical Ensemble Classifier},
  author={Shoaib Meraj Sami and Mohammed Imamul Hassan Bhuiyan},
  journal={2020 11th International Conference on Electrical and Computer Engineering (ICECE)},
  year={2020},
  pages={206-209}
}
  • S. Sami, M. Bhuiyan
  • Published 17 December 2020
  • Computer Science, Engineering, Mathematics
  • 2020 11th International Conference on Electrical and Computer Engineering (ICECE)
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… 
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Figures and Tables from this paper

Power Transformer Fault Diagnosis with Intrinsic Time-scale Decomposition and XGBoost Classifier
  • S. Sami, M. Bhuiyan
  • Computer Science, Engineering
    Lecture Notes on Data Engineering and Communications Technologies
  • 2021
TLDR
The proposed intrinsic time-scale decomposition (ITD)-based method for power transformer fault diagnosis achieves more than 95% accuracy and high sensitivity and F1-score, better than conventional methods and some recent machine learning-based fault diagnosis approaches.

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