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Author: search.filters.author.Pothisarn, C.Subject: search.filters.subject.Wavelet Transform

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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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    An application of discrete wavelet transform and support vector machines algorithm for classification of fault types on underground cable
    (2012-12-12)
    Ngaopitakkul, A.
    ;
    Pothisarn, C.
    ;
    Bunjongjit, S.
    ;
    Suechoey, B.
    This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify the fault types in underground distribution 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 underground distribution systems have been investigated so that the algorithm can be implemented. SVM is also compared with the coefficients DWT comparison technique. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission and distribution systems. © 2012 IEEE.
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    Improvement of algorithm to reduce training time of back-propagation neural network for transformer interturn fault location
    (2012-10-29)
    Ngaopitakkul, A.
    ;
    Pothisarn, C.
    ;
    Klomjit, J.
    ;
    Bunjongjit, S.
    ;
    Suechoey, B.
    This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for location of interturn faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using ATP/EMTP in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented by MATLAB. In addition, the choice of initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy. © 2012 IEEE.
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    Coefficient comparison technique of discrete wavelet transform for discriminating between external short circuit and internal winding fault in power transformer
    (2012-01-01)
    Pothisarn, C.
    ;
    Jettanasen, C.
    ;
    Klomjit, J.
    ;
    Ngaopitakkul, A.
    This paper proposes a technique for detecting and identifying internal winding fault of three-phase two-winding transformer which variations of coefficients of high frequency component obtained from DWT of differential current are analyzed. The maximum coefficient details of DWT are performed as comparison indicator in order to discriminate between internal fault and external short circuit. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. Results show that the proposed technique has good accuracy to detect fault and to identify its position in the considered system.
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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.