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Author: search.filters.author.Chiradeja, P.Subject: search.filters.subject.ATP/EMTP

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    Discrete wavelet transform and back-propagation neural networks algorithm for fault classification in underground cable
    (2011-07-26)
    Kaitwanidvilai, S.
    ;
    Pothisarn, C.
    ;
    Jettanasen, C.
    ;
    Chiradeja, P.
    ;
    Ngaopitakkul, A.
    This paper proposes a new technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for fault classifications on underground cable. Simulations and the training process for the back-propagation neural network are performed using ATP/EMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detect fault 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 results are shown that an average accuracy values obtained from BPNN can indicate the fault classification with satisfactory accuracy, and will be very useful in the development of a power system protection scheme.
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    Identification of the fault location for three-terminal transmission lines using discrete wavelet transforms
    (2009-12-16)
    Chiradeja, P.
    ;
    Pothisarn, C.
    This paper proposes a technique to detect fault locations in a three-bus transmission system using discrete wavelet transform (DWT). The comparison among the first peak time in first scale of each terminal (buses) that can detect fault is performed and the two fastest first peak time obtained from comparison are used as an input data for traveling wave equation later. A comparison of results obtained from three different types of mother wavelet is discussed in order to identify the fault locations with an application of traveling wave theory. It is shown that the db4 mother wavelet produces better results than those from 'sym4' and 'coif4', with a mean error of less than 400 m.