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Author: search.filters.author.Bunjongjit, S.Subject: search.filters.subject.Fault

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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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    Discrete wavelet transform and probabilistic neural networks algorithm for identification of fault locations on transmission systems
    (2004-12-01)
    Ngaopitakkul, A.
    ;
    Kunakorn, A.
    ;
    Bunjongjit, S.
    This paper proposes a new algorithm for detecting faults in an electric power transmission system. The Discrete Wavelet Transform (DWT) and probabilistic neural network (PNN) are used in order to detect the high frequency components and to identify fault locations on the transmission system. Simulations and the training process for the neural network are performed using ATP/EMTP and MATLAB. It is found that the proposed algorithm gives satisfactory results, and will be very useful in the development of a power system protection scheme. © 2004 IEEE.