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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.
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Internal failure analysis of transformer windings
(2012-12-31)
Tuethong, P.
;
Yutthagowith, P.
;
Kunakorn, A.
;
Potivejkul, S.
This paper presents internal failure analysis of transformer windings based on the characteristic of winding impedance in the frequency domain. Two winding samples are selected to test. The simple equivalent circuit of the transformer winding is investigated and proposed. The two windings under test have 76 turns with different length. An iron core is either inserted or not inserted inside a winding in the test to investigate the effect of the iron core. Turn to turn short circuit on the windings is considered as internal failure in this paper. The simulated turn to turn short on the model is carried out in the experiment to observe impedance characteristic in the frequency domain. A number and position of short turns are varied to observe characteristics of impedances The impedance of the models of the transformer windings are measured by a spectrum analyzer in a wide frequency range upto 2 MHz. From the test results, it is investigated that a resonance frequency of the transformer model is depended on a number and position of short turns. From the investigation, it has high possibility that the characteristic can be used for analysis of internal failures occurring in a transformer. © 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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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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Detecting winding to ground fault locations in power transformers using back-propagation neural networks
(2006-12-01)
Ngaopitakkul, A.
;
Kunakorn, A.
This paper presents an algorithm based on a combination of discrete wavelet transforms and neural networks for detecting locations of winding to ground faults in a two-winding three-phase transformer. The fault conditions of the transformer are simulated using ATP/EMTP in order to obtain fault current signals used as an input for a training process of a back-propagation neural network. The training process and fault diagnosis decision algorithm are implemented using toolboxes on MATLAB/Simulink. Various cases studies based on Thailand electricity transmission and distribution systems are performed to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a fault diagnosis process for a transformer manufacturer.
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Discrimination between external short circuits and internal faults in transformer windings using discrete wavelet transforms
(2005-12-01)
Ngaopitakkul, A.
;
Kunakorn, A.
;
Ngamroo, I.
In this paper, a technique for separation between internal faults in a two-winding three-phase transformer and external short circuits is presented. The fault detection algorithm is constructed on the basis of coefficient comparison from signals decomposed from Discrete Wavelet Transform. Computer simulations are performed using ATP/EMTP as well as MATLAB/Simulink. Various cases and fault types are studied to verify the validity of the algorithm. It is found that the proposed method gives a satisfactory accuracy, and will be particularly useful in a development of a modern differential relay for a transformer protection scheme. © 2005 IEEE.

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