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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
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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.
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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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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
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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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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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Discriminating among inrush current, external fault and internalwinding fault using coefficient of DWT
(2012-06-12)
Jettanasen, Chaiyan
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Klomjit, Jittiphong
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Yodkhuang, Apichart
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Ngaopitakkul, Atthapol
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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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