Ngaopitakkul, Atthapol

Preferred name

Alternative Name

Ngaopitakkul, A.

Main Affiliation

School of Engineering

atthapol.ng@kmitl.ac.th

Now showing 1 - 10 of 22

Some of the metrics are blocked by your
consent settings
A new directional relay algorithm for the protection of transmission network systems using discrete wavelet transforms
(2010-12-01)
Ngaopitakkul, A.
;
Kaitwanidvilai, S.
This paper proposes a novel directional relay algorithm to protect transmission network systems with an application of discrete wavelet transform (DWT). The fault signals are simulated using PSCAD/EMTDC. The coefficients of the positive sequence current obtained from each bus are compared in order to identify the direction of fault signals. The coefficient ratio between buses that the fault occurred is calculated so that the proper protective relay sequence can be selected. The result is shown that the algorithm is capable of performing the fault detection as well as arranging the protective relay sequence, with accurate results. ©2010 IEEE.
Some of the metrics are blocked by your
consent settings
Discrete wavelet transform for improving the accuracy of an unbalance current protection relay due to transient fault and inrush current signals
(2019-05-01)
Patcharoen, Theerasak
;
Ngaopitakkul, Atthapol
An unbalance current protection relay (60C, 51NC) cannot operate fast enough to avoid catastrophic failure against high system fault currents within capacitor units because traditional unbalance current protection relays are not capable of distinguishing and classifying transient fault current and switching inrush current. This paper proposes a new algorithm for the detection and classification of such current signals. The program used for the simulation was PSCAD/EMTDC. The current signals output was used as discrete wavelet transform (DWT) input. The wavelet-based fault and inrush current detection and classification unit received the various captured transient current signals, and used DWT to detect and analyse various cases studies. The actual inrush current signals from both the experimental setup and testing on an actual 115 kV HV shunt capacitor bank were used to verify the proposed algorithm. Results show that the proposed algorithm is effective at accurately identifying fault and inrush currents.
Some of the metrics are blocked by your
consent settings
Discrete wavelet transform and probabilistic neural network algorithm for classification of fault type in underground cable
(2012-01-01)
Ngaopitakkul, A.
;
Suttisinthong, N.
This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for classifying fault types on underground cable. Simulations and the training process for the PNN are performed using ATPIEMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The maximum coefficients of DWT of phase A, B, C and zero sequence for post-fault current waveforms are used as an input for the training pattern. Various cases studies based on Thailand electricity distribution underground systems have been investigated so that the algorithm can be implemented. The coefficients of DWT are also compared with those of PNN in this paper. The results show that the proposed algorithm is capable of performing the fault classification with satisfactory accuracy. © 2012 IEEE.
Some of the metrics are blocked by your
consent settings
Discrete wavelet transform and back-propagation neural networks algorithm for fault classification in underground cable
(2011-07-26)
Kaitwanidvilai, S.
;
Pothisarn, C.
;
Jettanasen, C.
;
Chiradeja, P.
;
Ngaopitakkul, A.
This paper proposes a new technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for fault classifications on underground cable. Simulations and the training process for the back-propagation neural network are performed using ATP/EMTP and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. Various cases studies based on Thailand electricity distribution underground systems have been investigated so that the algorithm can be implemented. The results are shown that an average accuracy values obtained from BPNN can indicate the fault classification with satisfactory accuracy, and will be very useful in the development of a power system protection scheme.
Some of the metrics are blocked by your
consent settings
The combination of discrete wavelet transform and fuzzy logic algorithm for fault classification on transmission system
(2012-10-01)
Ngaopitakkul, Atthapol
In the literature for fault classification, several decision algorithms have different solutions and techniques. The most research works have only considered the fault diagnosis for single bus systems and two-bus systems. In fact, transmission lines are connected to each other and become a large grid connected system. During faults, it is necessary for the protection system to deal with a complicated transmission network. This paper proposes a new technique using discrete wavelet transform (DWT) and Fuzzy Logic in order to identify the fault types on transmission systems. The DWT is used to detect the high frequency components from these signals. Positive sequence current signals are used in fault detection decision algorithm. The variations of first scale high frequency component that detects fault are used as an input for the fuzzy logic. Various cases studies based on Thailand electricity transmission systems have been investigated so that the algorithm can be implemented. Fuzzy logic is also compared with the comparison of the coefficients DWT technique. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems. ICIC International © 2012.
Some of the metrics are blocked by your
consent settings
Identification of fault location for simultaneous fault in distribution system using discrete wavelet transform
(2011-04-01)
Pothisarn, C.
;
Jettanasen, C.
;
Ngaopitakkul, A.
;
Apisit, C.
Currently, the most effective technique for identifying fault location, based on a travelling wave, has been proposed in several research papers. However, the effects of simultaneous faults have been neglected. In order to overcome this problem, a new algorithm will be developed in order to predict fault location precisely. This paper presents a technique to detect fault locations, during simultaneous fault, in an underground distribution system using discrete wavelet transform (DWT). The DWT is used to detect the high frequency components. The time that the fault signal uses to reach the ends of the distribution line is considered, then, applied so that the distance of fault can be calculated. The result is found that the proposed algorithm gives satisfactory both in case of single fault and simultaneous fault. ICIC International © 2011.
Some of the metrics are blocked by your
consent settings
Selection of proper activation functions in back-propagation neural networks algorithm for identifying the phase with fault appearance in transformer windings
(2012-06-01)
Ngaopitakkul, Atthapol
;
Jettanasen, Chaiyan
This paper presents an algorithm based on a combination of Discrete Wavelet Transforms and back-propagation neural networks for identifying the types of fault including the phase with fault appearance of a two-winding three-phase power 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. Various cases and fault types based on Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Various activation functions in each hidden layer and the output layer are compared in order to select the best activation function for identifying the types of internal fault of the transformer winding. It is found that average accuracy obtained from hyperbolic tangent-hyperbolic tangent-linear activation function gives satisfactory accuracy, and will be particularly useful in the development of a modern differential relay. © 2012 ICIC International.
Some of the metrics are blocked by your
consent settings
Selection of proper artificial neural networks for fault classification on single circuit transmission line
(2012-01-01)
Bunjongjit, Sulee
;
Ngaopitakkul, Atthapol
This paper proposes a new technique using discrete wavelet transform (DWT) and artificial neural networks for fault classification on single circuit transmission line. Simulation and the training process for the artificial neural networks are performed using ATP/EMTP and MATLAB respectively. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these current signals. Positive sequence current signals are employed in faults detection decision algorithm. The variations of first scale high frequency component detecting faults are employed as an input for the training process. Back-propagation (BP) neural network, Radial basis function (RBF) neural network and Probabilistic neural network (PNN) are compared in this paper. The results are shown that average accuracy values obtained from PNN give satisfactory results with less training time. © 2012 ICIC International.
Some of the metrics are blocked by your
consent settings
Identification of fault types for underground cable using discrete Wavelet transform
(2010-12-01)
Apisit, C.
;
Ngaopitakkul, A.
In this paper, a technique for identifying the phase with fault appearance in underground cable is presented. The Wavelet transform has been employed to extract high frequency components superimposed on fault signals simulated using ATP/EMTP. The coefficients obtained from the Wavelet transform are used in constructing a decision algorithm. Various cases have been investigated so that the algorithm can be implemented. It is found that the proposed method can indicate the fault types with satisfactory accuracy.
Some of the metrics are blocked by your
consent settings
Uncertainty of fault location prediction due to behavior of simultaneous fault in electrical power system using travelling wave theory
(2011-04-01)
Ngaopitakkul, A.
;
Apisit, C.
This paper presents uncertainty of fault location due to behavior of simultaneous fault in an electrical power transmission system. The fault signal is simulated using PSCAD/EMTDC. The Discrete Wavelet Transform (DWT) is used to detect the high frequency components. The variation of high frequency components of positive sequence current signals at the end of transmission line is considered. The results obtained from the analysis are used in order to locate faults with an application of travelling wave theory. It is found that the travelling wave theory cannot precisely locate simultaneous fault in an electrical power transmission system. © 2011 ISSN 2185-2766.

Ngaopitakkul, Atthapol

Filters

Settings

Sort By

Results per page