Pothisarn, Chaichan

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Pothisarn, Chaichan
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Pothisarn, C.
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chaichan.po@kmitl.ac.th

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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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    Analysis of electrical losses in transformers using artificial neural networks
    (2014-01-01)
    Suttisinthong, N.
    ;
    Pothisarn, C.
    This paper proposes a technique to analysis electrical losses in distribution transformers 1-phase 30 kVA using of back-propagation neural networks (BPNN). Experimental data at various temperature of transformers obtained from manufacturer, are employed as an input pattern for BPNN while output pattern which corresponding to total losses in transformers. The total number of test set are 150 sets in order to verify the validity of the proposes technique. The results show that average accuracy obtained from the proposes technique gives satisfactory accuracy.
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    Selection of proper activation functions in back-propagation neural network algorithm for single-circuit transmission line
    (2014-01-01)
    Suttisinthong, N.
    ;
    Seewirote, B.
    ;
    Ngaopitakkul, A.
    ;
    Pothisarn, C.
    This paper proposes an appropriate activation function for the fault classification decision algorithm. The decision algorithm based on the hybrid of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) has been proposed to classify the fault type. The DWT is employed to decompose high frequency component of current signals. The maximum coefficient from the first scale at 1/4 cycle of phase A, B, and C of post-fault current signals and zero sequence current obtained by the DWT have been used as an input variable in a decision algorithm. The activation functions in each hidden layer and output layer have been varied, and the results obtained from the decision algorithm have been investigated with the variation of fault inception angles, fault types, and fault locations. The results have illustrated that the use of Hyperbolic tangent sigmoid function in the first and the second layers with Linear function in the output layer is the most appropriate scheme for the transmission system.