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Author: search.filters.author.Ngaopitakkul, A.Subject: search.filters.subject.Discrete wavelet transform

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    Selection of proper activation functions in back-propagation neural network algorithm for single-circuit transmission line
    (2014-01-01)
    Suttisinthong, N.
    ;
    Seewirote, B.
    ;
    Ngaopitakkul, A.
    ;
    Pothisarn, C.
    This paper proposes an appropriate activation function for the fault classification decision algorithm. The decision algorithm based on the hybrid of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) has been proposed to classify the fault type. The DWT is employed to decompose high frequency component of current signals. The maximum coefficient from the first scale at 1/4 cycle of phase A, B, and C of post-fault current signals and zero sequence current obtained by the DWT have been used as an input variable in a decision algorithm. The activation functions in each hidden layer and output layer have been varied, and the results obtained from the decision algorithm have been investigated with the variation of fault inception angles, fault types, and fault locations. The results have illustrated that the use of Hyperbolic tangent sigmoid function in the first and the second layers with Linear function in the output layer is the most appropriate scheme for the transmission system.
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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.
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    Discrete wavelet transform and probabilistic neural networks algorithm for identification of fault locations on transmission systems
    (2004-12-01)
    Ngaopitakkul, A.
    ;
    Kunakorn, A.
    ;
    Bunjongjit, S.
    This paper proposes a new algorithm for detecting faults in an electric power transmission system. The Discrete Wavelet Transform (DWT) and probabilistic neural network (PNN) are used in order to detect the high frequency components and to identify fault locations on the transmission system. Simulations and the training process for the neural network are performed using ATP/EMTP and MATLAB. It is found that the proposed algorithm gives satisfactory results, and will be very useful in the development of a power system protection scheme. © 2004 IEEE.