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Author: search.filters.author.Kunakorn, A.Subject: search.filters.subject.Transformer windings

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    Detecting winding to ground fault locations in power transformers using back-propagation neural networks
    (2006-12-01)
    Ngaopitakkul, A.
    ;
    Kunakorn, A.
    This paper presents an algorithm based on a combination of discrete wavelet transforms and neural networks for detecting locations of winding to ground faults in a two-winding three-phase transformer. The fault conditions of the transformer are simulated using ATP/EMTP in order to obtain fault current signals used as an input for a training process of a back-propagation neural network. The training process and fault diagnosis decision algorithm are implemented using toolboxes on MATLAB/Simulink. Various cases studies based on Thailand electricity transmission and distribution systems are performed 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 fault diagnosis process for a transformer manufacturer.
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    Discrimination between external short circuits and internal faults in transformer windings using discrete wavelet transforms
    (2005-12-01)
    Ngaopitakkul, A.
    ;
    Kunakorn, A.
    ;
    Ngamroo, I.
    In this paper, a technique for separation between internal faults in a two-winding three-phase transformer and external short circuits is presented. The fault detection algorithm is constructed on the basis of coefficient comparison from signals decomposed from Discrete Wavelet Transform. Computer simulations are performed using ATP/EMTP as well as MATLAB/Simulink. Various cases and fault types 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. © 2005 IEEE.