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Author: search.filters.author.Seewirote, B.Has files: No

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    Feasibility study and impact of energy consumption reduction using T5 fluorescent lamp in building
    (2014-01-01)
    Suttisinthong, N.
    ;
    Seewirote, B.
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    Ngaopitakkul, A.
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    Jettanasen, C.
    In order to benefit the energy conservation in the building, efficient use of lighting in the building is one of the following actions. To reduce energy consumption by the lighting system, this paper proposes the feasibility and system impact study of energy saving using fluorescent T5 tube lamp in building by displaying the information as energy, illuminance including comparing fluorescent T8 tube lamp that are widely used in nowadays. The obtained results from the analysis will be useful to only choose and decide in the installation of fluorescent T5 tube lamp with electronic ballast for lighting system in building. © 2014 IEEE.
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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.
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    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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    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.
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    Klomjit, J.
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    Bunjongjit, S.
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    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.