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Author: search.filters.author.Pothisarn, ChaichanSubject: search.filters.subject.Fault detection

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    Development of overcurrent relay based on wavelet transform for fault detection in transmission line
    (2024-12-01)
    Ananwattanaporn, Santipont
    ;
    Lertwanitrot, Praikanok
    ;
    Ngaopitakkul, Atthapol
    ;
    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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    A phase diagram approach to the detection and location of faulty capacitor units in A 115-kV capacitor bank based on unbalanced current arguments
    (2023-03-01)
    Pothisarn, Chaichan
    ;
    Lertwanitrot, Praikanok
    ;
    Ngaopitakkul, Atthapol
    The protection of traditional high voltage capacitor banks relies on an unbalance relay which operates when an internal fuse is blown. However, the unbalance relay cannot indicate cause or position of the fault. Thus, an operator wastes time and human resources investigating the fault issues. To address this issue, a method to locate the fault position in a capacitor bank is developed in this study. The study was simulated by using PSCAD software and modeled on the 115-kV system of the Electricity Generating Authority of Thailand (EGAT). Case studies involving faults with varied phases, side and branch connections, row connections, and inception angles were considered. Moreover, the magnitude and argument of the current phase and unbalanced current were analyzed to identify the fault location in a capacitor bank. The performance of the proposed method was verified via comparison with that of traditional methods and the results of laboratory experiments. In addition, various voltage systems were observed to verify the flexibility and accuracy of the proposed method. The results demonstrate that the proposed method was more efficient than traditional methods at locating the fault position in a capacitor bank.
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    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)
    Chiradeja, Pathomthat
    ;
    Pothisarn, Chaichan
    ;
    Phannil, Nattanon
    ;
    Ananwattananporn, Santipont
    ;
    Leelajindakrairerk, Monthon
    Internal 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.