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Author: search.filters.author.Asfani, Dimas AntonHas files: No

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    Comparison of various mother wavelets for fault classification in electrical systems
    (2020-02-01)
    Pothisarn, Chaichan
    ;
    Klomjit, Jittiphong
    ;
    Ngaopitakkul, Atthapol
    ;
    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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    Application of discrete wavelet transform and back-propagation neural network for internal and external fault classification in transformer
    (2019-01-01)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    ;
    Asfani, Dimas Anton
    ;
    Negara, Yulistya
    This paper proposes an algorithm for internal and external fault discrimination in the three-phase two-winding power transformer based on a combination of discrete wavelet transform (DWT) and back-propagation neural network (BPNN). The maximum ratio obtained from division algorithm between DWT coefficient value of differential current and zero sequence component in post-fault condition differential current signals is employed as an input for the training pattern for BPNN in order to discriminate between internal fault and external short circuit. The proposed algorithm performance has been test using various cases studies based on Thailand electricity transmission and distribution systems data. Results show that the proposed technique can achieved satisfy accuracy for internal and external fault detection and discrimination in the considered system. This methodology and result can be used to further improve protection system of power transformer in the future.
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    Fault classification in transformer using low frequency component
    (2017-12-13)
    Jettanasen, Chaiyan
    ;
    Ngaopitakkul, Atthapol
    ;
    Asfani, Dimas Anton
    ;
    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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    Internal Fault Classification Algorithm in Power Transformer Based on Discrete Wavelet Transform and Fuzzy Logic
    (2017-11-15)
    Ananwattanaporn, Santipont
    ;
    Leelajindakrairerk, Monthon
    ;
    Jettanasen, Chaiyan
    ;
    Pothisarn, Chaichan
    ;
    Ngaopitakkul, Atthapol
    This paper proposed classification algorithm that combination of wavelet transform and fuzzy logic to classifying the internal fault type in power transform. The decision algorithm process, a structure of the fuzzy logic consists of 4 inputs and 1 output. The maximum ratio of DWT at & #xbc; cycle of phase A, B, C is performed as input variables while the output variables are designated corresponding to various types of internal faults. The 50 MVA, 115/23 kV three-phase power transformer has been modelled and simulate to evaluate the performance of proposed algorithm. The results show that the proposed algorithm gives satisfactory results, however, the overall accuracy indicates that this algorithm requires the further improvement.