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Analysis of harmonics in indoor Lighting System with LED and fluorescent luminaire
(2017-07-25)
Bunjongjit, S.
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Ngaopitakkul, A.
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Leelajindakrairerk, M.
Lighting system is an important part in everyday life. Today, lighting technology has been developing in many aspects. Technology such as Light Emitting Diode (LED) luminaire has been applied to indoor lighting system to replace conventional luminaire. LED lamps have many advantages such as less energy consumption, long life cycle and environmental friendly issue due to without toxic component. However, installation cost is main concern for investor regarding changing conventional lamp toward LED. So, the lighting system in some building are consist of both conventional and LED luminaires. In such a case the power quality must be taken into consideration. This paper aims to study power quality problem in term of harmonics generated by lighting system consisting of both fluorescent and LED luminaires. The obtained result can be used to design filter circuit for harmonic reduction in lighting system.
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Analysis of interturn fault characteristics in single phase transformer using experimental setup
(2017-01-30)
Bunjongjit, S.
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Klomjit, J.
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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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Improvement to reduce training time of back-propagation neural networks for discrimination between external short circuit and internal winding fault
(2014-11-05)
Bunjongjit, S.
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Ngaopitakkul, A.
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Pothisarn, C.
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Jettanasen, C.
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.
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An application of a discrete wavelet transform and a back-propagation neural network algorithm for fault diagnosis on single-circuit transmission line
(2013-09-01)
Ngaopitakkul, A.
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Bunjongjit, S.
This article proposes an application of the discrete wavelet transform (DWT) and back-propagation neural networks (BPNN) for fault diagnosis on single-circuit transmission line. ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is used to decompose the high-frequency component of these signals. In addition, characteristics of the fault current at various fault inception angles, fault locations and faulty phases are detailed. The DWT is employed in extracting the high frequency component contained in the fault currents, and the coefficients of the first scale from the DWT that can detect fault are investigated, and the decision algorithm is constructed based on the BPNN. The results show that the proposed technique provides satisfactory results. © 2013 Taylor & Francis Group, LLC.
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A discrete wavelet transform and fuzzy logic algorithm for identifying the location of fault in underground distribution system
(2013-01-01)
Bunjongjit, S.
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Ngaopitakkul, A.
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Pothisarn, C.
This paper proposes the hybrid decision algorithm of discrete wavelet transform (DWT) and fuzzy logic in order to identify the location of fault in underground distribution cable. The high frequency component obtained from DWT with the mother wavelet daubechies4 (db4) is used as an index for the occurrence of faults. The first peak time of DWT, obtained from positive sequence that can detected the occurrence of faults are considered as an input pattern of decision algorithm. The obtained average accuracy results have shown that the proposed decision algorithm is able to identify the location of fault with satisfactory accuracy. © 2013 IEEE.
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An application of discrete wavelet transform and support vector machines algorithm for classification of fault types on underground cable
(2012-12-12)
Ngaopitakkul, A.
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Pothisarn, C.
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Bunjongjit, S.
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Suechoey, B.
This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify the fault types in underground distribution systems. The DWT is used to detect the high frequency components from fault 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 SVM. Various cases studies based on Thailand electricity underground distribution systems have been investigated so that the algorithm can be implemented. SVM is also compared with the coefficients DWT comparison technique. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission and distribution systems. © 2012 IEEE.
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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.
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Klomjit, J.
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Positharn, C.
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Bunjongjit, S.
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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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Improvement of algorithm to reduce training time of back-propagation neural network for transformer interturn fault location
(2012-10-29)
Ngaopitakkul, A.
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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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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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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.
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Klomjit, J.
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Bunjongjit, S.
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Ngaopitakkul, A.
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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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