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Access type: UnknownSubject: search.filters.subject.Back-propagation neural network

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    Comparison of artificial intelligence methods for fault classification of the 115-kv hybrid transmission system
    (2020-06-01)
    Klomjit, Jittiphong
    ;
    Ngaopitakkul, Atthapol
    This research proposes a comparison study on different artificial intelligence (AI) methods for classifying faults in hybrid transmission line systems. The 115-kV hybrid transmission line in the Provincial Electricity Authority (PEA-Thailand) system, which is a single circuit single conductor transmission line, is studied. Fault signals in the transmission line were generated by the EMTP/ATPDraw software. Various factors such as fault location, type, and angle were considered. Then, fault signals were analyzed by coefficient details on the first scale of the discrete wavelet transform. Daubechies mother wavelet from MATLAB software was used to decompose the fault signal. The coefficient value of the mother wavelet behaved depending on the position, inception of fault angle, and fault type. AI methods including probabilistic neural networks (PNNs), back-propagation neural networks (BPNNs), and support vector machine (SVM) were used to identify faults. AI input used the maximum first peak coefficients of phase ABC and zero sequence. The results obtained from the study were found to be satisfactory with all AI methodologies having an average accuracy of more than 98% in the case study. However, the SVM technique can provide more accurate results than the PNN and BPNN techniques with less computation burden. Thus, it is suitable for being applied to actual protection systems.
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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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    Application of back-propagation neural network for transformer differential protection schemes part 2 identification the phase with fault appearance in power transformer
    (2012-12-01)
    Ngaopitakkul, Atthapol
    ;
    Pothisarn, Chaichan
    ;
    Bunjongjit, Sulee
    ;
    Klomjit, Jittiphong
    ;
    Suechoey, Boonlert
    In this paper, a decision algorithm for identifying the phase with fault appearance of a two-winding three-phase transformer has been proposed. A decision algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks (BPNN) is developed. Daubechies4 (db4) is employed as mother wavelet in order to decompose high frequency components from fault signals. The maximum coefficients of DWT at cycle of phase A, B, C and zero sequence for post-fault differential current are used as input patterns for training process, and the results obtained from the decision algorithm are investigated. Various cases and fault types are studied to verify the validity of the algorithm. The result is found that the proposed decision algorithm can give more satisfactory results. © 2012 IEEE.