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Artificial neural network approaches for fault classification: comparison and performance

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Abstract

This manuscript focuses the implementation of artificial neural network-based algorithms to classify different types of faults in a power transformer, meant particularly for NonDestructive Test for transformer fault classification. The performance analysis of Probabilistic Neural Network (PNN) and Backpropagation Network classifiers has been carried out using the database of dissolved gases collected from Punjab State Electricity Board, Patiala, India. Features from the preprocessed data have been extracted using dimensionality reduction technique, i.e., principal component analysis. The selected features were used as inputs to the Backpropagation Network and PNN classifiers. A comparative study of the two intelligent classifiers has been carried out, which reveals that PNN classifier outperforms the Backpropagation Network classifier.

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Acknowledgments

The authors are thankful to Dr. B. N. Chudasama, Assistant Professor, School of Physics and Material Science, Thapar University, for his valuable suggestions.

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Authors and Affiliations

  1. Department of Electrical and Instrumentation Engineering, Thapar University, Patiala, 147004, India

    Tapsi Nagpal

  2. Department of Electrical Engineering, Guru Nanak Dev Engineering College, Ludhiana, 141006, India

    Yadwinder Singh Brar

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  1. Tapsi Nagpal
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  2. Yadwinder Singh Brar
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Correspondence to Tapsi Nagpal.

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Nagpal, T., Brar, Y.S. Artificial neural network approaches for fault classification: comparison and performance. Neural Comput & Applic 25, 1863–1870 (2014). https://doi.org/10.1007/s00521-014-1677-y

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