Ngaopitakkul, Atthapol
Loading...
Preferred name
Ngaopitakkul, Atthapol
Alternative Name
Ngaopitakkul, A.
Main Affiliation
Email
atthapol.ng@kmitl.ac.th
12 results
Now showing 1 - 10 of 12
- Some of the metrics are blocked by yourconsent settings
Item type:Publication, Fault classification in transformer using low frequency component(2017-12-13); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Internal Fault Classification Algorithm in Power Transformer Based on Discrete Wavelet Transform and Fuzzy Logic(2017-11-15); ; ; This paper proposed classification algorithm that combination of wavelet transform and fuzzy logic to classifying the internal fault type in power transform. The decision algorithm process, a structure of the fuzzy logic consists of 4 inputs and 1 output. The maximum ratio of DWT at & #xbc; cycle of phase A, B, C is performed as input variables while the output variables are designated corresponding to various types of internal faults. The 50 MVA, 115/23 kV three-phase power transformer has been modelled and simulate to evaluate the performance of proposed algorithm. The results show that the proposed algorithm gives satisfactory results, however, the overall accuracy indicates that this algorithm requires the further improvement. - 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, Comparison of mother wavelet for classification fault on hybrid transmission line systems(2017-07-01); Sreewirote, BanchaThis paper proposes comparison mother wavelets for fault classification on hybrid transmission line systems. Hybrid system consists of overhead line and underground cable of 115 kV. ATP/EMTP software has been used for generating fault signals. Then it varies location of fault, fault type and angle. Current signals and zero sequence are analyzed by Discrete Wavelet Transform (DWT) in MATLAB software. DWT decomposes high frequency components from fault signals. Coefficient in scale 1 has been decomposed from Mother Wavelets such as Daubechies (db), Symlets (sym), Biorthogonal (bior) and Coiflets (coif). The coefficient for any mother wavelet has same behavior but different value. Design algorithm for fault classification and compare the result. Therefore, comparison of mother wavelet for fault classification is important to provide the high accuracy. Daubechies (db) can give accuracy more than any mother wavelet. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Behaviour of interturn fault in power transformer winding using high frequency components of discrete wavelet transform(2012-12-01) - 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, Improvement to reduce training time of back-propagation neural networks for discrimination between external short circuit and internal winding fault(2014-11-05); ; This paper proposes the improvement technique to reduce training time of back-propagation neural network. The decision algorithm based on the hybrid of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) has been proposed to classify between external fault and internal fault in power transformer. The DWT is employed to decompose high frequency component of post-fault differential current signals and used as an input pattern for the training process of a neural network in a decision algorithm with a use of the BPNN. The proposed technique is compared with conventional training process of BPNN in terms of average accuracy and training time process. The obtained results show that the proposed technique can reduce of training process duration time and is very effective in classifying between external fault and internal fault in power transformer with satisfactory accuracy. - 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, Discriminating among inrush current, external fault and internal fault in power transformer using low frequency components comparison of DWT(2012-12-01); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Behavior analysis of winding to ground fault in transformer using high and low frequency components from discrete wavelet transform(2017-07-21)