Ngaopitakkul, Atthapol

Preferred name

Alternative Name

Ngaopitakkul, A.

Main Affiliation

School of Engineering

atthapol.ng@kmitl.ac.th

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Fault classification in transformer using low frequency component
(2017-12-13)
Jettanasen, Chaiyan
;
Ngaopitakkul, Atthapol
;
Asfani, Dimas Anton
;
Negara, I. Made Yulistya
Transform is a vital equipment in power system that need protection system in order to provide fast and correct response when disturbance occur in system. So, this paper proposed internal and external fault classification in Transformer using algorithm based on discrete wavelet transform (DWT). Low frequency component from DWT has been used to create condition for algorithm. The proposed algorithm has been test using transmission line connected to transformer experimental setup on laboratory level. The result from proposed algorithm shown satisfactory result with 100% accuracy in both internal and external fault in transmission line connected transformer system.
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Differential protection schemes for classification of fault detection between external fault and internal winding fault in transformer using probabilistic neural network
(2012-12-01)
Jettanasen, C.
;
Klomjit, J.
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Positharn, C.
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Bunjongjit, S.
;
Ngaopitakkul, A.
This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for discriminating between external fault and internal winding fault in power transformer. The coefficients of the first scale from the DWT that can detect fault are investigated. The maximum coefficients details (cD1) from DWT in first scale at 1/4 cycle of phase A, B, C and zero sequence for post-fault differential current waveforms have been used as an input for the training process of the PNN in a decision algorithm. Various cases studies based on Thailand electricity transmission and distribution systems have been investigated so that the algorithm can be implemented. The results show that the proposed algorithm is capable of performing the fault detection with satisfactory accuracy. © 2012 IEEE.
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Discriminating among inrush current, external fault and internalwinding fault using coefficient of DWT
(2012-06-12)
Jettanasen, Chaiyan
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Klomjit, Jittiphong
;
Yodkhuang, Apichart
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Ngaopitakkul, Atthapol
;
Pothisarn, Chaichan
This paper proposes a technique for discriminating among inrush current, external fault and 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. 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 in the considered system.
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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
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Pothisarn, Chaichan
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Bunjongjit, Sulee
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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.
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Improvement of internal fault detection algorithms to reduce training time of back-propagation neural networks for transformer differential protection schemes
(2012-01-01)
Bunjongjit, S.
;
Ngaopitakkul, A.
This paper presents an algorithm based on a combination of Discrete Wavelet Transforms (DWT) and back-propagation neural networks for detection and classification of internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using Electromagnetic Transients Program (EMTP) in order to obtain current signals. The training process for the neural network and fault diagnosis decision are implemented on MATLAB. In addition, the initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy. © 2012 Praise Worthy Prize S.r.l. - All rights reserved.
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Discriminating among inrush current, external fault and internal fault in power transformer using low frequency components comparison of DWT
(2012-12-01)
Jettanasen, Chaiyan
;
Pothisarn, Chaichan
;
Klomjit, Jittiphong
;
Ngaopitakkul, Atthapol
A technique using discrete wavelet transform (DWT) in order to discriminate among inrush current, internal fault, and external fault has been proposed. Daubechies4 (db4) is employed as mother wavelet in order to decompose low frequency components from fault signals. A ratio between per unit differential current and per unit time is calculated and performed as comparison indicator. The results obtained from the proposed technique have good accuracy to discriminating fault in the considered system. © 2012 IEEJ Industry Appl Soc.
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Improvement of algorithm to reduce training time of back-propagation neural network for transformer interturn fault location
(2012-10-29)
Ngaopitakkul, A.
;
Pothisarn, C.
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Klomjit, J.
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Bunjongjit, S.
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Suechoey, B.
This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for location of interturn 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 by MATLAB. In addition, the choice of initial number of neurons for the first hidden layer to decrease duration time of train process is taken into account. A comparison between the proposed technique and conventional training is presented. The result is shown that the proposed technique is very effective in reduce training time and gives a satisfactory accuracy. © 2012 IEEE.
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Discriminating between external short circuit and internal winding fault in power transformer using rbf neural networks
(2013-07-12)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
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Jettanasen, Chaiyan
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Pothisarn, Chaichan
;
Thongsuk, Surakit
In the literature for fault detection, several decision algorithms have been developed to be employed in the protective relay. In previous research works, the behaviour analysis of signals is performed using DWT. The results obtained from the analysis will be useful in the development of a detected fault scheme for power transformer in this paper. This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and radial basis function neural network (RBFNN) 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.
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Analysis of interturn fault characteristics in single phase transformer using experimental setup
(2017-01-30)
Bunjongjit, S.
;
Klomjit, J.
;
Ngaopitakkul, A.
This paper aims to study characteristics of interturn fault in a single-phase transformer using an experimental setup. Conventional dry-type single-phase transformer rated of 15 kVA and voltage of 220/440 V has been used in the experiment. The winding of transformer was separated as three sub-coils and to evaluate characteristics of interturn fault, the short-circuit between the sub-coil of high voltage side has been performed as the interturn fault. The obtained results show that the behavior of the winding fault is important for developing a fault detection scheme.
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Comparison of various mother wavelets for fault classification in electrical systems
(2020-02-01)
Pothisarn, Chaichan
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Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
;
Jettanasen, Chaiyan
;
Asfani, Dimas Anton
This paper presents a comparative study on mother wavelets using a fault type classification algorithm in a power system. The study aims to evaluate the performance of the protection algorithm by implementing different mother wavelets for signal analysis and determines a suitable mother wavelet for power system protection applications. The factors that influence the fault signal, such as the fault location, fault type, and inception angle, have been considered during testing. The algorithm operates by applying the discrete wavelet transform (DWT) to the three-phase current and zero-sequence signal obtained from the experimental setup. The DWT extracts high-frequency components from the signals during both the normal and fault states. The coefficients at scales 1-3 have been decomposed using different mother wavelets, such as Daubechies (db), symlets (sym), biorthogonal (bior), and Coiflets (coif). The results reveal different coefficient values for the different mother wavelets even though the behaviors are similar. The coefficient for any mother wavelet has the same behavior but does not have the same value. Therefore, this finding has shown that the mother wavelet has a significant impact on the accuracy of the fault classification algorithm.

Ngaopitakkul, Atthapol

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