KMITL

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

Browse

Settings

Author: search.filters.author.Jettanasen, ChaiyanSubject: search.filters.subject.Transformer windings

Search Results

Now showing 1 - 5 of 5
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    A novel probabilistic neural network-based algorithm for classifying internal fault in transformer windings
    (2013-01-01)
    Jettanasen, Chaiyan
    ;
    Ngaopitakkul, Atthapol
    The major function of protective devices in a power system is to detect the occurrence of faults and to isolate the faulty sections from the rest of the system. Much progress has been made in the development algorithms for detecting faults in power transformers, which depend on transients-based techniques. This paper presents an algorithm based on a combination of discrete wavelet transforms and probabilistic neural networks (PNNs) for classifying internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using alternative transients program/electromagnetic transients program (ATP/EMTP) in order to obtain current signals. The mother wavelet Daubechies4 is employed to decompose the high-frequency components from these signals. All three phases of the differential current signals are used in the fault detection decision algorithm. The variations of first-scale high-frequency component that detects fault are used as an input for the training pattern. The training process for the neural network and fault diagnosis decision is implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on the Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Backpropagation neural network is also compared with the PNN in this paper. It is found that the proposed method gives satisfactory accuracy with less training time, and will be particularly useful in the development of a modern differential relay for a transformer protection scheme. © 2013 Institute of Electrical Engineers of Japan.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Application of back-propagation neural network for transformer differential protection schemes part 1 discrimination between external short circuit and internal winding fault
    (2012-12-01)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    ;
    Klomjit, Jittiphong
    ;
    Pothisarn, Chaichan
    ;
    Seewirote, Buncha
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for discriminating between external fault and internal winding fault of three-phase two-winding transformer. The DWT is employed for extracting the high frequency component contained in the post-fault differential current waveforms, and the coefficients of the first scale from the DWT that can detect fault are investigated as an input for the training pattern. 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 is highly satisfactory. © 2012 IEEE.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Selection of proper activation functions in back-propagation neural networks algorithm for transformer internal fault locations
    (2012-12-01)
    Jettanasen, Chaiyan
    ;
    Pothisarn, Chaichan
    ;
    Bunjongjit, Sulee
    ;
    Ngaopitakkul, Atthapol
    ;
    Suechoey, Boonlert
    This paper presents an analysis on the selection of an appropriate activation function used in neural networks for locating the internal fault in a two-winding three-phase transformer. A decision algorithm based on a combination of Discrete Wavelet Transforms and neural networks is developed. 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 using toolboxes on MATLAB/Simulink. Various activation functions in hidden layers and output layers are compared in order to find out and to select the best activation function for indicating the position of internal faults of the winding transformer for the winding to ground faults. It is found that the use of Hyperbolic tangent-function for the hidden layers, and Linear activation function for the output layer gives the most satisfactory accuracy in these particular case studies. © 2012 IEEE.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Selection of proper activation functions in back-propagation neural networks algorithm for identifying the phase with fault appearance in transformer windings
    (2012-06-01)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for identifying the types of fault including the phase with fault appearance of a two-winding three-phase power 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 using toolboxes on MATLAB. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Various activation functions in each hidden layer and the output layer are compared in order to select the best activation function for identifying the types of internal fault of the transformer winding. It is found that average accuracy obtained from hyperbolic tangent-hyperbolic tangent-linear activation function gives satisfactory accuracy, and will be particularly useful in the development of a modern differential relay. © 2012 ICIC International.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Behavior of interturn fault in transformer windings using discrete wavelet transform
    (2010-12-01)
    Jettanasen, Chaiyan
    ;
    Ngaopitakkul, Atthapol
    ;
    Apisit, Chaowat
    In the literature for fault detection, most of research works have never been mentioned about the transformer models with the high frequency model including capacitances of the transformer. This paper presents behaviour of winding to ground fault signals in a three-phase two-winding transformer. The advantage of the discrete wavelet transform (DWT) is that the band of analysis can be fine adjusted so that high frequency components and low frequency components are detected precisely; that is why discrete wavelet transform is herein considered. The fault is simulated using ATP/EMTP and the behaviour analysis of signals is performed using DWT. The variation of high frequency components of differential current signals is proposed in this paper. The results obtained from the analysis will be useful in the development of a detect fault scheme for power transformer in the future.