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Author: search.filters.author.Suttisinthong, N.Subject: search.filters.subject.Power transformer

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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.
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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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    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.