Ngaopitakkul, Atthapol

Loading...
Thumbnail Image
Preferred name
Ngaopitakkul, Atthapol
Alternative Name
Ngaopitakkul, A.
Main Affiliation
Email
atthapol.ng@kmitl.ac.th

Settings

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Coefficient comparison technique of discrete wavelet transform for discriminating between external short circuit and internal winding fault in power transformer
    (2012-01-01)
    Pothisarn, C.
    ;
    Jettanasen, C.
    ;
    Klomjit, J.
    ;
    Ngaopitakkul, A.
    This paper proposes a technique for detecting and identifying internal winding fault of three-phase two-winding transformer which variations of coefficients of high frequency component obtained from DWT of differential current are analyzed. The maximum coefficient details of DWT are performed as comparison indicator in order to discriminate between internal fault and external short circuit. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. Results show that the proposed technique has good accuracy to detect fault and to identify its position in the considered system.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Behaviour of internal fault and external fault in power transformer using discrete wavelet transform
    (2012-06-12)
    Ngaopitakkul, Atthapol
    ;
    Pothisarn, Chaichan
    The differential protection is aimed at detecting internal faults in transformer windings. Therefore, it is necessary to understand fault behaviour before doing performing decision algorithm. This paper presents behaviour of internal winding fault and external short circuit in a three-phase two-winding transformer. The advantage of the discrete wavelet transform (DWT) is considered herein. The fault is simulated using ATP/EMTP and the behaviour analysis of signals is performed using DWT. The variation of high frequency components of differential current signals is presented in this paper. The results obtained from the analysis will be useful in the development of a fault detecting scheme for power transformer in the future.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Publication,
    Discrimination between external short circuit and internal winding fault in power transformer using discrete wavelet transform and back-propagation neural network
    (2012-01-01)
    Jettanasen, C.
    ;
    Klomjit, J.
    ;
    Bunjongjit, S.
    ;
    Ngaopitakkul, A.
    ;
    Suechoey, B.
    This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for detecting and identifying internal winding fault of three-phase two-winding transformer. The maximum ratio obtained from division algorithm between coefficient from DWT of differential current and zero sequence for post-fault differential current waveforms is employed as an input for the training pattern in order to discriminate between internal fault and external short circuit. 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 has good accuracy to detect fault and to identify its position in the considered system. © 2012 IEEE.