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Application of discrete wavelet transform and back-propagation neural network for internal and external fault classification in transformer
(2019-01-01)
Ngaopitakkul, Atthapol
;
Jettanasen, Chaiyan
;
Asfani, Dimas Anton
;
Negara, Yulistya
This paper proposes an algorithm for internal and external fault discrimination in the three-phase two-winding power transformer based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN). The maximum ratio obtained from division algorithm between DWT coefficient value of differential current and zero sequence component in post-fault condition differential current signals is employed as an input for the training pattern for BPNN in order to discriminate between internal fault and external short circuit. The proposed algorithm performance has been test using various cases studies based on Thailand electricity transmission and distribution systems data. Results show that the proposed technique can achieved satisfy accuracy for internal and external fault detection and discrimination in the considered system. This methodology and result can be used to further improve protection system of power transformer in the future.
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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.
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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.

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