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Neural Computing and Applications

Erschienen in:

01.12.2014 | Original Article

Artificial neural network approaches for fault classification: comparison and performance

verfasst von: Tapsi Nagpal, Yadwinder Singh Brar

Erschienen in: Neural Computing and Applications | Ausgabe 7-8/2014

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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.

Vorheriger Artikel Rainfall data analyzing using moving average (MA) model and wavelet multi-resolution intelligent model for noise evaluation to improve the forecasting accuracy

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Titel: Artificial neural network approaches for fault classification: comparison and performance
verfasst von: Tapsi Nagpal
Yadwinder Singh Brar
Publikationsdatum: 01.12.2014
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 7-8/2014
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1677-y

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