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Author: search.filters.author.Chiradeja, P.Subject: search.filters.subject.Wavelet transform

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    Prediction of fault location in overhead transmission line and underground distribution cable using probabilistic neural network
    (2013-01-01)
    Chiradeja, P.
    ;
    Ngaopitakkul, A.
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for locating fault on transmission and distribution system. Simulations and the training process for the PNN are performed using Electromagnetic Transients Program (EMTP) and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from fault signals. The first peak time in first scale of each bus, that can detect fault, is used as input pattern 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. The results show that the proposed algorithm is capable of performing the fault location with satisfactory accuracy. © 2013 Praise Worthy Prize S.r.l. - All rights reserved.
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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.