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Fault classification in transformer using low frequency component
(2017-12-13)
Jettanasen, Chaiyan
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Ngaopitakkul, Atthapol
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Asfani, Dimas Anton
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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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Internal Fault Classification Algorithm in Power Transformer Based on Discrete Wavelet Transform and Fuzzy Logic
(2017-11-15)
Ananwattanaporn, Santipont
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Leelajindakrairerk, Monthon
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Jettanasen, Chaiyan
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Pothisarn, Chaichan
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Ngaopitakkul, Atthapol
This paper proposed classification algorithm that combination of wavelet transform and fuzzy logic to classifying the internal fault type in power transform. The decision algorithm process, a structure of the fuzzy logic consists of 4 inputs and 1 output. The maximum ratio of DWT at & #xbc; cycle of phase A, B, C is performed as input variables while the output variables are designated corresponding to various types of internal faults. The 50 MVA, 115/23 kV three-phase power transformer has been modelled and simulate to evaluate the performance of proposed algorithm. The results show that the proposed algorithm gives satisfactory results, however, the overall accuracy indicates that this algorithm requires the further improvement.
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Behavior analysis of winding to ground fault in transformer using high and low frequency components from discrete wavelet transform
(2017-07-21)
Rummdkarn, Jintasit
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Ngaopitakkul, Atthapol
This paper analyzes the behavior of winding to ground fault in the single-phase transformer using experimental setup in laboratory level. The winding to ground fault in each voltage winding of the single-phase transformer is first obtained in order to analyze the behavior of the differential current of the transformer. For different winding voltages of the transformer, the calculated differential current of the transformer will be employed to analyze the behavior of winding fault to ground in the single-phase transformer using discrete wavelet transform (DWT) method. The coefficient comparison between high frequency component and low frequency component will be done in order to confirm the effectiveness of the applied technique in this study.
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Comparison of mother wavelet for classification fault on hybrid transmission line systems
(2017-07-01)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
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Sreewirote, Bancha
This paper proposes comparison mother wavelets for fault classification on hybrid transmission line systems. Hybrid system consists of overhead line and underground cable of 115 kV. ATP/EMTP software has been used for generating fault signals. Then it varies location of fault, fault type and angle. Current signals and zero sequence are analyzed by Discrete Wavelet Transform (DWT) in MATLAB software. DWT decomposes high frequency components from fault signals. Coefficient in scale 1 has been decomposed from Mother Wavelets such as Daubechies (db), Symlets (sym), Biorthogonal (bior) and Coiflets (coif). The coefficient for any mother wavelet has same behavior but different value. Design algorithm for fault classification and compare the result. Therefore, comparison of mother wavelet for fault classification is important to provide the high accuracy. Daubechies (db) can give accuracy more than any mother wavelet.
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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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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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Analysis of characteristics using wavelet transform for simultaneous faults in electrical power system
(2012-12-12)
Pothisarn, C.
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Ngaopitakkul, A.
This paper presents an analysis of characteristics for simultaneous fault signals in a 500-kV electrical power transmission system using wavelet transform. Such fault signals can occur in the transmission system, and have an effect to the operation of distance relays installed in the system. The fault analysis is performed using PSCAD/EMTDC. The Discrete Wavelet Transform (DWT) is used in order to detect the high frequency components. In addition, characteristics of fault current at various fault inception angles, fault locations and faulty phases are detailed. It has been found that when applying the previous decision algorithm give a wrong conclusion of fault types and fault location. © 2012 IEEE.
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Behaviour of interturn fault in power transformer winding using high frequency components of discrete wavelet transform
(2012-12-01)
Klomjit, Jittiphong
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Ngaopitakkul, Atthapol
Behaviour of interturn winding fault signals in a three-phase two-winding transformer with delta connected primary and wye connected secondary, using high frequency components of DWT is proposed in this paper. The mother wavelet daubechies4 (db4) is employed to decompose high frequency components from signals. Various case studies have been done including the variation of fault inception angles, fault types, and fault locations. The result will be useful in the development of a fault detecting scheme for power transformer in the future. © 2012 IEEJ Industry Appl Soc.
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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
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Pothisarn, Chaichan
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Klomjit, Jittiphong
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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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Analysis of characteristics of simultaneous faults in electrical power systems using wavelet transform
(2008-12-01)
Ngaopitakkul, A.
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Pongchaisrikul, W.
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Kunakorn, A.
This paper presents an algorithm used in the analysis of simultaneous fault characteristics. The system under investigations is the 500-kV transmission network in Thailand. The analysis is performed using PSCAD/EMTDC and MATLAB/Simulink. Wavelet transform is used in order to detect high frequency components of the fault current signals. The characteristics of the fault current with various fault inception angles, fault locations and faulty phases are observed. It is found that the technique proposed in this paper gives satisfactory results in the simultaneous fault classification. © 2008 IEEE.

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