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Author: search.filters.author.Pothisarn, ChaichanSubject: search.filters.subject.Transformer windings

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    Selection of proper activation functions in back-propagation neural networks algorithm for transformer internal fault locations
    (2012-12-01)
    Jettanasen, Chaiyan
    ;
    Pothisarn, Chaichan
    ;
    Bunjongjit, Sulee
    ;
    Ngaopitakkul, Atthapol
    ;
    Suechoey, Boonlert
    This paper presents an analysis on the selection of an appropriate activation function used in neural networks for locating the internal fault in a two-winding three-phase transformer. A decision algorithm based on a combination of Discrete Wavelet Transforms and neural networks is developed. 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 activation functions in hidden layers and output layers are compared in order to find out and to select the best activation function for indicating the position of internal faults of the winding transformer for the winding to ground faults. It is found that the use of Hyperbolic tangent-function for the hidden layers, and Linear activation function for the output layer gives the most satisfactory accuracy in these particular case studies. © 2012 IEEE.
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    Application of back-propagation neural network for transformer differential protection schemes part 1 discrimination between external short circuit and internal winding fault
    (2012-12-01)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    ;
    Klomjit, Jittiphong
    ;
    Pothisarn, Chaichan
    ;
    Seewirote, Buncha
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for discriminating between external fault and internal winding fault of three-phase two-winding transformer. The DWT is employed for extracting the high frequency component contained in the post-fault differential current waveforms, and the coefficients of the first scale from the DWT that can detect fault are investigated as an input for the training pattern. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. Results show that the proposed technique is highly satisfactory. © 2012 IEEE.
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    Behavior of winding to ground fault in transformer windings using discrete wavelet transform
    (2010-12-01)
    Pothisarn, Chaichan
    ;
    Ngaopitakkul, Atthapol
    ;
    Pongchaisrikul, Worravut
    ;
    Apisit, Chaowat
    ;
    Jonpermpoonpol, Saridroje
    In the literature for fault detection, most of research works have never been mentioned about the high frequency transformer including capacitances. This paper presents behaviour of winding to ground fault signals in a three-phase two-winding transformer. The advantage of the discrete wavelet transform (DWT) is that the band of analysis can be fine adjusted so that high frequency components and low frequency components are detected precisely, consequently DWT is considered herein. The fault is simulated using ATP/EMTP and the behaviour analysis of signals is performed using DWT. The variation of high frequency components of differential current signals is proposed in this paper. The results obtained from the analysis will be useful in the development of a detect fault scheme for power transformer in the future.