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Author: search.filters.author.Pothisarn, C.Subject: search.filters.subject.Fault Location

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    A discrete wavelet transform and fuzzy logic algorithm for identifying the location of fault in underground distribution system
    (2013-01-01)
    Bunjongjit, S.
    ;
    Ngaopitakkul, A.
    ;
    Pothisarn, C.
    This paper proposes the hybrid decision algorithm of discrete wavelet transform (DWT) and fuzzy logic in order to identify the location of fault in underground distribution cable. The high frequency component obtained from DWT with the mother wavelet daubechies4 (db4) is used as an index for the occurrence of faults. The first peak time of DWT, obtained from positive sequence that can detected the occurrence of faults are considered as an input pattern of decision algorithm. The obtained average accuracy results have shown that the proposed decision algorithm is able to identify the location of fault with satisfactory accuracy. © 2013 IEEE.
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    An application of discrete wavelet transform and support vector machines algorithm for fault locations in underground cable
    (2012-12-12)
    Apisit, C.
    ;
    Pothisarn, C.
    ;
    Ngaopitakkul, A.
    This paper proposes a technique using discrete wavelet transform (DWT) and support vector machines (SVM) for fault location in underground distribution cable. 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 maximum coefficient obtained from positive sequence current in first scale capable of detecting fault of each bus is used as input pattern for the training pattern. It is shown that the proposed technique gives satisfactory results, and will be very useful in the development of a power system protection scheme. © 2012 IEEE.
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    The combination of discrete wavelet transform and self organizing map for identification of fault location on transmission line
    (2012-01-01)
    Pothisarn, C.
    ;
    Ngaopitakkul, A.
    In the literature for fault location, Artificial neural networks (ANNs) have been reported. At the present time, unsupervised learning is not well understood. This paper proposes a new algorithm for identifying fault location on transmission lines, using Discrete Wavelet Transform (DWT) and Self-organizing maps (SOMs). The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The coefficients of scalel obtained using the DWT are used for training and test processes of the SOMs. After the training process, case studies are varied. The result shows that the average accuracy obtained from combination of DWT and SOMs is satisfactory.