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Development of overcurrent relay based on wavelet transform for fault detection in transmission line
(2024-12-01)
Ananwattanaporn, Santipont
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Lertwanitrot, Praikanok
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Ngaopitakkul, Atthapol
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Pothisarn, Chaichan
This study proposes a protection relay using a microcontroller to detect and classify faults in transmission lines based on the wavelet transform. An experimental model was constructed from an actual 115 kV transmission system prototype. The current signal was observed based on the fault type, phase, and position. Clark’s transform and the discrete wavelet transform (DWT) were applied to transform signals for analysis. Moreover, the performance of fault detection based on the output signals of Clark’s transform (alpha sequence, beta sequence, and zero sequence current) was compared to the performance of the alternative proposed fault detection method, which is based on the combining factor between alpha and beta sequence current. In addition, the influence of DWT level on fault analysis is also considered and is used to confirm the accuracy of fault detection. Results show that the proposed method is efficient for fault detection and classification. This finding allows the researcher to choose the appropriate analytical method. Moreover, it can also be used as the basis for overcurrent relay algorithm design in the effort to develop more advanced technologies.
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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
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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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Identifying types of simultaneous fault in transmission line using discrete wavelet transform and fuzzy logic algorithm
(2013-07-17)
Ngaopitakkul, Atthapol
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Apisit, Chaowat
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Bunjongjit, Sulee
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Pothisarn, Chaichan
In the literature for fault classification, several decision algorithms have different solutions and techniques. These research works have been rarely mentioned about simultaneous faults in transmission systems. This paper presents the decision algorithm for identifying types of simultaneous fault along the transmission line. Decision algorithms based on discrete wavelet transform (DWT) and fuzzy logic are investigated. The analysis of fault signals is performed using DWT. The DWT is used in order to detect the high frequency components. The coefficient details (phase A, B, C and zero sequence of post-fault current signals) of DWT at the first peak time that positive sequence current can detect fault, are performed as an input for the fuzzy logic. The result shows that the accuracy of the proposed algorithm is highly satisfactory. © 2013 ICIC International.
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Selection of proper artificial neural networks for fault classification on single circuit transmission line
(2012-01-01)
Bunjongjit, Sulee
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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.
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Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line
(2008-01-01)
Ngaopitakkul, Atthapol
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Pothisarn, Chaichan
This paper proposes a technique using discrete wavelet transform (DWT) and back-propagation neural network (BPNN) for locating of fault location on single circuit transmission lines. The ATP/EMTP was used to simulated fault signals. The mother wavelet daubechies4 (db4) is employed to decompose, high frequency component from these signals. The first peak time in first scale of each bus that can detect fault are used as input pattern for the training pattern. It is shown that the proposed technique gives satisfactory. © 2008 IEEE.

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