KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Author: search.filters.author.Suttisinthong, N.

Search Results

Now showing 1 - 6 of 6
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Feasibility study and impact of energy consumption reduction using T5 fluorescent lamp in building
    (2014-01-01)
    Suttisinthong, N.
    ;
    Seewirote, B.
    ;
    Ngaopitakkul, A.
    ;
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Discrete wavelet transform and probabilistic neural network algorithm for classification of fault type in underground cable
    (2012-01-01)
    Ngaopitakkul, A.
    ;
    Suttisinthong, N.
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for classifying fault types on underground cable. Simulations and the training process for the PNN are performed using ATPIEMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The maximum coefficients of DWT of phase A, B, C and zero sequence for post-fault current waveforms are used as an input for the training pattern. Various cases studies based on Thailand electricity distribution underground systems have been investigated so that the algorithm can be implemented. The coefficients of DWT are also compared with those of PNN in this paper. The results show that the proposed algorithm is capable of performing the fault classification with satisfactory accuracy. © 2012 IEEE.