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Author: search.filters.author.Bunjongjit, S.Subject: search.filters.subject.Wavelet Transform

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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.
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    Pothisarn, C.
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    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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    Differential protection schemes for classification of fault detection between external fault and internal winding fault in transformer using probabilistic neural network
    (2012-12-01)
    Jettanasen, C.
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    Klomjit, J.
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    Positharn, C.
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    Bunjongjit, S.
    ;
    Ngaopitakkul, A.
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for discriminating between external fault and internal winding fault in power transformer. The coefficients of the first scale from the DWT that can detect fault are investigated. The maximum coefficients details (cD1) from DWT in first scale at 1/4 cycle of phase A, B, C and zero sequence for post-fault differential current waveforms have been used as an input for the training process of the PNN in a decision algorithm. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. The results show that the proposed algorithm is capable of performing the fault detection with satisfactory accuracy. © 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.
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    Klomjit, J.
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    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.