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Author: search.filters.author.Bunjongjit, SuleeSubject: search.filters.subject.Wavelet transform

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    Identifying types of simultaneous fault in transmission line using discrete wavelet transform and fuzzy logic algorithm
    (2013-07-17)
    Ngaopitakkul, Atthapol
    ;
    Apisit, Chaowat
    ;
    Bunjongjit, Sulee
    ;
    Pothisarn, Chaichan
    In the literature for fault classification, several decision algorithms have different solutions and techniques. These research works have been rarely mentioned about simultaneous faults in transmission systems. This paper presents the decision algorithm for identifying types of simultaneous fault along the transmission line. Decision algorithms based on discrete wavelet transform (DWT) and fuzzy logic are investigated. The analysis of fault signals is performed using DWT. The DWT is used in order to detect the high frequency components. The coefficient details (phase A, B, C and zero sequence of post-fault current signals) of DWT at the first peak time that positive sequence current can detect fault, are performed as an input for the fuzzy logic. The result shows that the accuracy of the proposed algorithm is highly satisfactory. © 2013 ICIC International.
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    Selection of proper artificial neural networks for fault classification on single circuit transmission line
    (2012-01-01)
    Bunjongjit, Sulee
    ;
    Ngaopitakkul, Atthapol
    This paper proposes a new technique using discrete wavelet transform (DWT) and artificial neural networks for fault classification on single circuit transmission line. Simulation and the training process for the artificial neural networks are performed using ATP/EMTP and MATLAB respectively. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these current signals. Positive sequence current signals are employed in faults detection decision algorithm. The variations of first scale high frequency component detecting faults are employed as an input for the training process. Back-propagation (BP) neural network, Radial basis function (RBF) neural network and Probabilistic neural network (PNN) are compared in this paper. The results are shown that average accuracy values obtained from PNN give satisfactory results with less training time. © 2012 ICIC International.
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    Fault diagnosis in transmission lines using wavelet transform analysis
    (2002-12-01)
    Makming, Pongsak
    ;
    Bunjongjit, Sulee
    ;
    Kunakorn, Anantawat
    ;
    Jiriwibhakorn, Somchart
    ;
    Kando, M.
    This paper presents a new method to diagnose faults in a transmission system, This is based on detecting high frequency components contained in a fault signal spectrum. The Discrete Wavelet Transform (DWT) is used in the analysis in order to classify fault types and to locate fault positions. Simulations are performed using ATP/EMTP. It is found that the proposed method gives satisfactory results, and will be very useful in the development of a power system protection scheme.