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Author: search.filters.author.Jettanasen, ChaiyanSubject: search.filters.subject.Power transformer

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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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    Application of support vector machines algorithm for discriminating between external fault and internal winding fault in power transformer
    (2014-02-04)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    ;
    Leelajindakrairerk, Monthon
    ;
    Pothisarn, Chaichan
    ;
    Suechoey, Boonlert
    The differential relaying principle is used for protection of medium and large power transformers. In the past decade, several decision algorithms have different solutions and techniques. This paper proposes a new technique using discrete wavelet transform (DWT) and support vector machines (SVM) to classify and discriminate between external fault and internal fault in power transformer. The DWT is used to detect the high frequency components from fault signals. The variations of first scale high frequency component that detects fault are used as input for the SVM. The proposed method gives satisfactory accuracy, and will be very useful in the development of a modern protection scheme for electrical power transmission systems. © 2014 ICIC International.
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    A discrete wavelet transform approach to discriminating among inrush current, external fault, and internal fault in power transformer using low-frequency components differential current only
    (2014-01-01)
    Ngaopitakkul, Atthapol
    ;
    Jettanasen, Chaiyan
    This paper proposes an algorithm based on discrete wavelet transform (DWT) for discriminating among inrush current, internal fault, and external fault in power transformers. Fault conditions are simulated using the Alternative Transients Program/Electromagnetic Transients Program (ATP/EMTP). Daubechies4 (db4) is employed as the mother wavelet to decompose low-frequency components from fault signals. The ratio between per unit (p.u.) differential current and p.u. time is suggested as an index. The numerator of the ratio is the difference between the maximum differential current and the minimum differential current in terms of p.u. with a base value selected at the transformer-rated current. The ratio is calculated for all three phases, and from a trial and error process the indices for the separation among the internal fault condition, the external fault condition, and inrush condition are defined. The results obtained from the proposed technique show good accuracy for discriminating faults in the considered system. In addition, the proposed algorithm uses data of the differential current with a time of quarter cycle under the analysis. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.
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    Behavior of interturn fault in transformer windings using discrete wavelet transform
    (2010-12-01)
    Jettanasen, Chaiyan
    ;
    Ngaopitakkul, Atthapol
    ;
    Apisit, Chaowat
    In the literature for fault detection, most of research works have never been mentioned about the transformer models with the high frequency model including capacitances of the transformer. This paper presents behaviour of winding to ground fault signals in a three-phase two-winding transformer. The advantage of the discrete wavelet transform (DWT) is that the band of analysis can be fine adjusted so that high frequency components and low frequency components are detected precisely; that is why discrete wavelet transform is herein considered. The fault is simulated using ATP/EMTP and the behaviour analysis of signals is performed using DWT. The variation of high frequency components of differential current signals is proposed in this paper. The results obtained from the analysis will be useful in the development of a detect fault scheme for power transformer in the future.