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Prediction of fault location in overhead transmission line and underground distribution cable using probabilistic neural network
(2013-01-01)
Chiradeja, P.
;
Ngaopitakkul, A.
This paper proposes an algorithm based on a combination of discrete wavelet transform (DWT) and probabilistic neural network (PNN) for locating fault on transmission and distribution system. Simulations and the training process for the PNN are performed using Electromagnetic Transients Program (EMTP) and MATLAB. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from fault signals. The first peak time in first scale of each bus, that can detect fault, is used as input pattern 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. The results show that the proposed algorithm is capable of performing the fault location with satisfactory accuracy. © 2013 Praise Worthy Prize S.r.l. - All rights reserved.
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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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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.
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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.
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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.
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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.
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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.
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Identification of simultaneous fault types in electrical power system using the comparisons technique of DWT coefficients
(2011-04-01)
Ngaopitakkul, A.
;
Apisit, C.
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Jettanasen, C.
;
Pothisarn, C.
This paper is aimed to propose a novel distance relay algorithm, which includes the effect of simultaneous fault. The previous research paper can give an uncertain result in a fault classification during simultaneous faults. In order to overcome this problem, the comparison of coefficients of discrete wavelet transform (DWT) has been developed. The fault analysis is performed using PSCAD/EMTDC. The current waveforms obtained from PSCAD/EMTDC are extracted to several scales with the wavelet transform, and the coefficients of the first scale from the wavelet transform are investigated. The coefficient details of DWT at the first peak time that positive sequence current can detect fault, are performed as comparison indicator in order to classify fault types. It is found that the technique proposed in this paper gives satisfactory results in the simultaneous fault classification. © 2011 ISSN 2185-2766.
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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.
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Coefficient comparison technique for identifying the fault types in underground cable
(2010-12-01)
Ngaopitakkul, A.
;
Kaitwanidvilai, S.
;
Apisit, C.
This paper proposes a novel comparison technique for identifying the phase with fault appearance in underground cable using Discrete Wavelet Transform. The Wavelet transform has been employed to extract high frequency components superimposed on fault signals simulated using ATP/EMTP. The fault type algorithm is constructed on the basis of coefficient comparison from signals decomposed from Discrete Wavelet Transform. Various cases studies based on Thailand electricity distribution underground systems 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. ©2010 IEEE.
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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.

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