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Subject: search.filters.subject.Transmission Line

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    Fault classification in transformer using low frequency component
    (2017-12-13)
    Jettanasen, Chaiyan
    ;
    Ngaopitakkul, Atthapol
    ;
    Asfani, Dimas Anton
    ;
    Negara, I. Made Yulistya
    Transform is a vital equipment in power system that need protection system in order to provide fast and correct response when disturbance occur in system. So, this paper proposed internal and external fault classification in Transformer using algorithm based on discrete wavelet transform (DWT). Low frequency component from DWT has been used to create condition for algorithm. The proposed algorithm has been test using transmission line connected to transformer experimental setup on laboratory level. The result from proposed algorithm shown satisfactory result with 100% accuracy in both internal and external fault in transmission line connected transformer system.
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    Comparison of mother wavelet for classification fault on hybrid transmission line systems
    (2017-07-01)
    Klomjit, Jittiphong
    ;
    Ngaopitakkul, Atthapol
    ;
    Sreewirote, Bancha
    This paper proposes comparison mother wavelets for fault classification on hybrid transmission line systems. Hybrid system consists of overhead line and underground cable of 115 kV. ATP/EMTP software has been used for generating fault signals. Then it varies location of fault, fault type and angle. Current signals and zero sequence are analyzed by Discrete Wavelet Transform (DWT) in MATLAB software. DWT decomposes high frequency components from fault signals. Coefficient in scale 1 has been decomposed from Mother Wavelets such as Daubechies (db), Symlets (sym), Biorthogonal (bior) and Coiflets (coif). The coefficient for any mother wavelet has same behavior but different value. Design algorithm for fault classification and compare the result. Therefore, comparison of mother wavelet for fault classification is important to provide the high accuracy. Daubechies (db) can give accuracy more than any mother wavelet.
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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.
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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.