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Author: search.filters.author.Ngaopitakkul, A.Subject: search.filters.subject.Neural network

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    Improvement of internal fault detection algorithms to reduce training time of back-propagation neural networks for transformer differential protection schemes
    (2012-01-01)
    Bunjongjit, S.
    ;
    Ngaopitakkul, A.
    This paper presents an algorithm based on a combination of Discrete Wavelet Transforms (DWT) and back-propagation neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using Electromagnetic Transients Program (EMTP) in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented on MATLAB. In addition, the initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy. © 2012 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.
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    Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line
    (2009-12-16)
    Pothisarn, C.
    ;
    Ngaopitakkul, A.
    This paper proposes a technique using Discrete Wavelet Transform (DWT) and Back-Propagation Neural Network (BPNN) to identify the fault types on single circuit transmission lines. The ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The result has shown that the proposed technique gives satisfactory results.