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Artificial Intelligence Tools Aided-decision for Power Transformer Fault Diagnosis

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International Journal of Computer Applications
© 2012 by IJCA Journal
Volume 38 - Number 3
Year of Publication: 2012
Authors:
Seifeddine Souahlia
Khmais Bacha
Abdelkader Chaari
10.5120/4665-6768

Seifeddine Souahlia, Khmais Bacha and Abdelkader Chaari. Article: Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis. International Journal of Computer Applications 38(3):1-8, January 2012. Full text available. BibTeX

@article{key:article,
	author = {Seifeddine Souahlia and Khmais Bacha and Abdelkader Chaari},
	title = {Article: Artificial Intelligence Tools Aided-Decision For Power Transformer Fault Diagnosis},
	journal = {International Journal of Computer Applications},
	year = {2012},
	volume = {38},
	number = {3},
	pages = {1-8},
	month = {January},
	note = {Full text available}
}

Abstract

This paper presents an intelligent fault classification approach for power transformer dissolved gas analysis (DGA). Fault diagnosis methods by the DGA and artificial intelligence (AI) techniques are implemented to improve the interpretation accuracy for DGA of power transformers. The DGA traditional methods are utilized to choose the most appropriate gas signature. AI techniques are applied to establish classification features for faults in the transformers based on the collected gas data. The features are applied as input data to fuzzy logic, artificial neural network (ANN) and support vector machine (SVM) classifiers for faults classification. The experimental data from Tunisian Company of Electricity and Gas (STEG) is used to evaluate the performance of proposed method. The results of the various DGA methods are classified using AI techniques and the results are compared with the empirical test. In comparison to the results obtained from the AI techniques, the ratios DGA method has been shown to possess the most excellent performance in identifying the transformer fault type. The test results indicate that the SVM approach can significantly improve the diagnosis accuracies for power transformer fault classification..

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