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Author: search.filters.author.Suttisinthong, N.Subject: search.filters.subject.Wavelet transform

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    Discrimination between external short circuit and internal winding fault in power transformer using discrete wavelet transform and back-propagation neural network
    (2012-01-01)
    Jettanasen, C.
    ;
    Klomjit, J.
    ;
    Bunjongjit, S.
    ;
    Ngaopitakkul, A.
    ;
    Suechoey, B.
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for detecting and identifying internal winding fault of three-phase two-winding transformer. The maximum ratio obtained from division algorithm between coefficient from DWT of differential current and zero sequence for post-fault differential current waveforms is employed as an input for the training pattern in order to discriminate between internal fault and external short circuit. 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 has good accuracy to detect fault and to identify its position in the considered system. © 2012 IEEE.
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    Discrete wavelet transform and probabilistic neural network algorithm for classification of fault type in underground cable
    (2012-01-01)
    Ngaopitakkul, A.
    ;
    Suttisinthong, N.
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for classifying fault types on underground cable. Simulations and the training process for the PNN are performed using ATPIEMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The maximum coefficients of DWT of phase A, B, C and zero sequence for post-fault current waveforms are used as an input for the training pattern. Various cases studies based on Thailand electricity distribution underground systems have been investigated so that the algorithm can be implemented. The coefficients of DWT are also compared with those of PNN in this paper. The results show that the proposed algorithm is capable of performing the fault classification with satisfactory accuracy. © 2012 IEEE.