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Internal Fault Classification in Transformer Windings using Combination of Discrete Wavelet-Transforms and Back-propagation Neural Networks  

Ngaopitakkul Atthapol (Department of Electrical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang)
Kunakorn Anantawat (Department of Electrical Engineering, Faculty of Engineering, King Mongkut's Institute of Technology Ladkrabang)
Publication Information
International Journal of Control, Automation, and Systems / v.4, no.3, 2006 , pp. 365-371 More about this Journal
Abstract
This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme.
Keywords
Discrete wavelet transforms; internal faults; neural network; transformer windings;
Citations & Related Records

Times Cited By Web Of Science : 10  (Related Records In Web of Science)
Times Cited By SCOPUS : 15
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