Kunakorn, Anantawat

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Kunakorn, Anantawat
Alternative Name
Kunakorn, A.
Kunakorn, Anatawat
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Email
anantawat.ku@kmitl.ac.th

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    Estimation of short circuit current due to a group of induction motors using an aggregation model
    (2004-12-01)
    Suwanwej, Wanlop
    ;
    Kunakorn, Anantawat
    This paper presents generalized equations for determining an aggregation model to represent a group of induction motors connected in the same bus of industrial power systems. The model is employed in calculating short circuit current contribution with different types of faults from the group of induction motors. The simulation and analysis are performed using PSCAD/EMTDC. Various case studies are used to implement the model. It is found that the aggregation model proposed in the paper can give reasonable and satisfactory results for short circuit contribution due to the induction motor loads. © 2004 IEEE.
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    Identification of fault types for a three-bus transmission network using Discrete Wavelet Transform and probabilistic neural networks
    (2007-12-01)
    Patcharoen, T.
    ;
    Ngaopitakkul, A.
    ;
    Kunakorn, A.
    This paper proposes a new algorithm for detecting faults in an electric power transmission network 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 types on a three-bus transmission network with a loop structure. Simulations and the training process for the neural network are performed using PSCAD/EMTDC and MATLAB. It is found that the proposed algorithm gives satisfactory results, and will be very useful in the development of a modern protection scheme for electrical power transmission network systems. © 2007 RPS.
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    Analysis of characteristics of simultaneous faults in electrical power systems using wavelet transform
    (2008-12-01)
    Ngaopitakkul, A.
    ;
    Pongchaisrikul, W.
    ;
    Kunakorn, A.
    This paper presents an algorithm used in the analysis of simultaneous fault characteristics. The system under investigations is the 500-kV transmission network in Thailand. The analysis is performed using PSCAD/EMTDC and MATLAB/Simulink. Wavelet transform is used in order to detect high frequency components of the fault current signals. The characteristics of the fault current with various fault inception angles, fault locations and faulty phases are observed. It is found that the technique proposed in this paper gives satisfactory results in the simultaneous fault classification. © 2008 IEEE.