Pothisarn, Chaichan
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Preferred name
Pothisarn, Chaichan
Alternative Name
Pothisarn, C.
Main Affiliation
Email
chaichan.po@kmitl.ac.th
7 results
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Item type:Publication, Identification of the fault location for three-terminal transmission lines using discrete wavelet transforms(2009-12-16) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrete wavelet transform and back-propagation neural networks algorithm for fault classification on transmission line(2009-12-16); This paper proposes a technique using Discrete Wavelet Transform (DWT) and Back-Propagation Neural Network (BPNN) to identify the fault types on single circuit transmission lines. The ATP/EMTP is used to simulate fault signals. The mother wavelet daubechies4 (db4) is employed to decompose high frequency component from these signals. The variations of first scale high frequency component that detect fault are used as an input for the training pattern. The result has shown that the proposed technique gives satisfactory results. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Development of overcurrent relay based on wavelet transform for fault detection in transmission line(2024-12-01); ; 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An experimental setup investigation to study characteristics of fault on transmission system(2015-01-01); ; This paper proposes the experimental setup for studying the characteristics of fault caused by balance and unbalance on a transmission system. The parameters of transmission system (inductance and capacitance) are calculated based on forms of transmission tower, size of conductor, types of conductor and arrangement of transmission line and, they normalized to obtain the values in the π- equivalent circuit model at voltage level of 400 V. In addition, the ATP/EMTP is used to compare the simulated results with the experimental setup in order to show the advantage of the experimental setup. The obtained results show that the similarity between the two waveforms. The experimental setup will be useful in the development of short-circuit protection system in laboratory. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Discrete wavelet transform and back-propagation neural networks algorithm for fault location on single-circuit transmission line(2008-01-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Comparison of various mother wavelets for fault classification in electrical systems(2020-02-01); ; ; Asfani, Dimas AntonThis 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Identifying types of simultaneous fault in transmission line using discrete wavelet transform and fuzzy logic algorithm(2013-07-17);