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Author: search.filters.author.Klomjit, J.

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    Analysis of interturn fault characteristics in single phase transformer using experimental setup
    (2017-01-30)
    Bunjongjit, S.
    ;
    Klomjit, J.
    ;
    Ngaopitakkul, A.
    This paper aims to study characteristics of interturn fault in a single-phase transformer using an experimental setup. Conventional dry-type single-phase transformer rated of 15 kVA and voltage of 220/440 V has been used in the experiment. The winding of transformer was separated as three sub-coils and to evaluate characteristics of interturn fault, the short-circuit between the sub-coil of high voltage side has been performed as the interturn fault. The obtained results show that the behavior of the winding fault is important for developing a fault detection scheme.
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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.
    ;
    Klomjit, J.
    ;
    Positharn, C.
    ;
    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.
    ;
    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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    Discrimination between external short circuit and internal winding fault in power transformer using discrete wavelet transform and back-propagation neural network
    (2012-01-01)
    Jettanasen, C.
    ;
    Klomjit, J.
    ;
    Bunjongjit, S.
    ;
    Ngaopitakkul, A.
    ;
    Suechoey, B.
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for detecting and identifying internal winding fault of three-phase two-winding transformer. The maximum ratio obtained from division algorithm between coefficient from DWT of differential current and zero sequence for post-fault differential current waveforms is employed as an input for the training pattern in order to discriminate between internal fault and external short circuit. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. Results show that the proposed technique has good accuracy to detect fault and to identify its position in the considered system. © 2012 IEEE.