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Author: search.filters.author.Kunadumrongrath, K.Subject: search.filters.subject.Transmission Line

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    Discrete wavelet transform and support vector machines algorithm for classification of fault types on transmission line
    (2012-01-01)
    Kunadumrongrath, K.
    ;
    Ngaopitakkul, A.
    This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify the fault types on transmission systems. The DWT is used to detect the high frequency components from fault signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detects fault are used as an input for the SVM. Various cases studies based on Thailand electricity transmission systems have been investigated so that the algorithm can be implemented. SVM is also compared with the comparison of the coefficients DWT technique as well as back-propagation neural network algorithm. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems.
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    Discrete wavelet transform and support vector machines algorithm for fault locations on single circuit transmission line
    (2012-01-01)
    Kunadumrongrath, K.
    ;
    Ngaopitakkul, A.
    This paper proposes a technique using discrete wavelet transform (DWT) and support vector machines (SVM) for fault location 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 first peak time 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.