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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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A novel probabilistic neural network-based algorithm for classifying internal fault in transformer windings
(2013-01-01)
Jettanasen, Chaiyan
;
Ngaopitakkul, Atthapol
The major function of protective devices in a power system is to detect the occurrence of faults and to isolate the faulty sections from the rest of the system. Much progress has been made in the development algorithms for detecting faults in power transformers, which depend on transients-based techniques. This paper presents an algorithm based on a combination of discrete wavelet transforms and probabilistic neural networks (PNNs) for classifying internal faults in a two-winding three-phase transformer. Fault conditions of the transformer are simulated using alternative transients program/electromagnetic transients program (ATP/EMTP) in order to obtain current signals. The mother wavelet Daubechies4 is employed to decompose the high-frequency components from these signals. All three phases of the differential current signals are used in the fault detection decision algorithm. The variations of first-scale high-frequency component that detects fault are used as an input for the training pattern. The training process for the neural network and fault diagnosis decision is implemented using toolboxes on MATLAB/Simulink. Various cases and fault types based on the Thailand electricity transmission and distribution systems are studied to verify the validity of the algorithm. Backpropagation neural network is also compared with the PNN in this paper. It is found that the proposed method gives satisfactory accuracy with less training time, and will be particularly useful in the development of a modern differential relay for a transformer protection scheme. © 2013 Institute of Electrical Engineers of Japan.
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Discriminating among inrush current, external fault and internalwinding fault using coefficient of DWT
(2012-06-12)
Jettanasen, Chaiyan
;
Klomjit, Jittiphong
;
Yodkhuang, Apichart
;
Ngaopitakkul, Atthapol
;
Pothisarn, Chaichan
This paper proposes a technique for discriminating among inrush current, external fault and internal winding fault of three-phase two-winding transformer which variations of coefficients of high frequency component obtained from DWT of differential current are analyzed. The maximum coefficient details of DWT are performed as comparison indicator. Various cases based on Thailand electricity transmission and distribution systems are studied to verify the validity of the proposed algorithm. Results show that the proposed technique has good accuracy in the considered system.
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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.

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