Ananwattanaporn, Santipont
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Preferred name
Ananwattanaporn, Santipont
Alternative Name
Ananwattanaporn, S.
Ananwattananporn, Santipont
Main Affiliation
Email
santipont.an@kmitl.ac.th
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Item type:Publication, Comparative Study for Discrete Wavelet Transform Between Single and Double Detection Approach to Fault Classification on Transmission Line(2024-01-01); This study presented a novelty method for a protection relay using a microcontroller. The method able to detect and classify faults in high voltage transmission lines based on the Discrete Wavelet transform (DWT). In addition, novelty of proposed method when compared to traditional methods was a signal analysis process applied Clark's Transformation and Double Detection Technique. A performance of proposed method was verified by created experimental model in our KMITL laboratory. All fault types (SL, LL, DLG and 3P) were observed. This result of this study found that not only applied DWT method can improved the accuracy of signal analysis, applied Clark's Transformation able to filter noise of signal, without loss of information. Meanwhile, Double Detection Technique able to re-confirm the accuracy of fault detection and classify. Therefore, it results in the performance of a protection relay increase which is beneficial to the electrical system. It can increase the accuracy and reliability of the system. - 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, Application of probabilistic neural networks using high-frequency components’ differential current for transformer protection schemes to discriminate between external faults and internal winding faults in power transformers(2021-11-01); ; Leelajindakrairerk, MonthonInternal and external faults in a power transformer are discriminated in this paper using an algorithm based on a combination of a discrete wavelet transform (DWT) and a probabilistic neural network (PNN). DWT decomposes high-frequency fault components using the maximum coefficients of a 1/4 cycle DWT as input patterns for the training process in a decision algorithm. A division algorithm between a zero sequence of post-fault differential current waveforms and the differential current coefficient in the 1/4 cycle DWT is used to detect the maximum ratio and faults. The simulation system uses various study cases based on Thailand’s electricity transmission and distribution systems. The simulation results demonstrated that the PNN and BPNN are effectively implemented and perform fault detection with satisfactory accuracy. However, the PNN method is most suitable for detecting internal and external faults, and the maximum coefficient algorithm is the most effective in detecting the fault. This study will be useful in differential protection for power transformers.