Ngaopitakkul, Atthapol
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Preferred name
Ngaopitakkul, Atthapol
Alternative Name
Ngaopitakkul, A.
Main Affiliation
Email
atthapol.ng@kmitl.ac.th
8 results
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Item type:Publication, Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line(2009-12-16); This paper proposes a technique using Discrete Wavelet Transform (DWT) and Back-Propagation Neural Network (BPNN) to identify the fault types on single circuit transmission lines. The ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The result has shown that the proposed technique gives satisfactory results. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrimination between external short circuits and internal faults in transformer windings using discrete wavelet transforms(2005-12-01); ; 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identification of fault types for a three-bus transmission network using Discrete Wavelet Transform and probabilistic neural networks(2007-12-01); This paper proposes a new algorithm for detecting faults in an electric power transmission network system. The Discrete Wavelet Transform (DWT) and probabilistic neural network (PNN) are used in order to detect the high frequency components and to identify fault types on a three-bus transmission network with a loop structure. Simulations and the training process for the neural network are performed using PSCAD/EMTDC and MATLAB. It is found that the proposed algorithm gives satisfactory results, and will be very useful in the development of a modern protection scheme for electrical power transmission network systems. © 2007 RPS. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line(2008-01-01); This paper proposes a technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for locating of fault location on single circuit transmission lines. The ATP/EMTP was used to simulated fault signals. The mother wavelet daubechies4 (db4) is employed to decompose, high frequency component from these signals. The first peak time in first scale of each bus that can detect fault are used as input pattern for the training pattern. It is shown that the proposed technique gives satisfactory. © 2008 IEEE. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Analysis of characteristics of simultaneous faults in electrical power systems using wavelet transform(2008-12-01); - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Internal fault classification in transformer windings using combination of discrete wavelet transforms and back-propagation neural networks(2006-06-01); This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and neural networks for detection and classification of internal 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 using toolboxes on MATLAB/Simulink. Various cases and fault types based on Thailand electricity transmission and distribution systems 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. Discrete wavelet transforms, internal faults, neural network, transformer windings. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Detecting winding to ground fault locations in power transformers using back-propagation neural networks(2006-12-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrete wavelet transform and probabilistic neural networks algorithm for identification of fault locations on transmission systems(2004-12-01); ; Bunjongjit, S.This paper proposes a new algorithm for detecting faults in an electric power transmission system. The Discrete Wavelet Transform (DWT) and probabilistic neural network (PNN) are used in order to detect the high frequency components and to identify fault locations on the transmission system. Simulations and the training process for the neural network are performed using ATP/EMTP and MATLAB. It is found that the proposed algorithm gives satisfactory results, and will be very useful in the development of a power system protection scheme. © 2004 IEEE.