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Internal fault classification in transformer windings using combination of discrete wavelet transforms and back-propagation neural networks
Date Issued
June 1, 2006
Type
Article
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. Discrete wavelet transforms, internal faults, neural network, transformer windings.
Citation
International Journal of Control Automation and Systems, 4(3), 365-371, 2006