Localization of turn-to-turn fault in transformers using Artificial Neural Networks and winding transfer function

Faridi, M. Ayub; Rahimpour, Ebrahim; Kharezi, M.; Mirzaei, Hassan Reza; Akbari, Asghar

doi:10.1109/icsd.2010.5568131

Cited by 24 publications

(13 citation statements)

References 3 publications

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“…They explain which parameters of the electric circuit of winding will change because of a specified fault and have not explained for an occurred winding fault, how it can be found out and whether the fault is one RD, AD. Faridi et al [18] and Firoozi et al [19] have detected only the location of SC and do not study the type and level of the fault either. A pattern-based method has been suggested in [20] for classification of SC faults in a distribution transformer using the graphical information of its winding TF.…”

Section: Introductionmentioning

confidence: 98%

Transformer winding faults classification based on transfer function analysis by support vector machine

Bigdeli

Vakilian

Rahimpour³

2012

IET Electr. Power Appl.

124

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This study presents an intelligent fault classification method for identification of transformer winding fault through transfer function (TF) analysis. For this analysis support vector machine (SVM) is used. The required data for training and testing of SVM are obtained by measurement on two groups of transformers (one is a classic 20 kV transformer and the other is a model transformer) under intact condition and under different fault conditions (axial displacement, radial deformation, disc space variation and short circuit of winding). Two different features extracted from the measured TFs are then used as the inputs to SVM classifier for fault classification. The accuracy of proposed method is compared with the accuracy of past well-known works. This comparison indicates that the proposed method can be used as a reliable method for transformer winding fault recognition.

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Section: Introductionmentioning

confidence: 98%

Transformer winding faults classification based on transfer function analysis by support vector machine

Bigdeli

Vakilian

Rahimpour³

2012

IET Electr. Power Appl.

124

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“…The important winding faults, which are most likely to be detected using the TF analysis, can be classified as follows: Axial displacement (AD) , Radial deformation (RD) , Disc space variation (DSV) , and Short circuit (SC) .…”

Section: Introductionmentioning

confidence: 99%

“…(1) Axial displacement (AD) [6][7][8][9][10], (2) Radial deformation (RD) [10][11][12], (3) Disc space variation (DSV) [13,14], and (4) Short circuit (SC) [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

“…These faults have been studied analytically and experimentally in [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. These researches have studied the sensitivity of TF parameters against those faults individually; however, the methods for detection of a specific winding fault and their classification into one of the AD, RD, DSV, or SC types are not discussed.…”

Section: Introductionmentioning

confidence: 99%

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A probabilistic neural network classifier-based method for transformer winding fault identification through its transfer function measurement

Bigdeli

Vakilian

Rahimpour³

2011

Int. Trans. Electr. Energ. Syst.

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SUMMARY In this paper, a new method is introduced for identification of transformer winding fault through transfer function analysis. For this analysis, vector fitting and probabilistic neural network are used. The results of transfer functions estimation through vector fitting are employed for training of neural network, and consequently, probabilistic neural network is used for classification of faults. The required data for fault type identification are obtained by measurements on two groups of transformers (one is a classic 20 kV transformer, and the other is a model transformer) under intact condition and under different fault conditions (axial displacement, radial deformation, disc space variation, and short circuit of winding). Comparing the results of this new method with the well‐known published work in the literature, the superior capabilities of this proposed method are demonstrated. Copyright © 2011 John Wiley & Sons, Ltd.

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“…In accordance to the principle of the multi-winding transformer circuit, magnetic circuit and equivalent circuit, the linear model of impedance matching balance transformer in equation (1) can be obtained through a series of simplified processing [7]. In equation (1), K=N1/N2 and x1 is the short circuit reactance of high voltage side, other notations see the reference [8]. …”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis System Identification Based on Impedance Matching Balance Transformer

Yan-jun¹,

Deng²

2016

Proceedings of the 2016 International Conference on Advanced Materials Science and Environmental Engineering

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Abstract-Impedance matching balance transformer is often used as the main electrified railway traction substation transformer. In order to solve the problem that traditional fault diagnosis method could not accurately identify the internal or external failure, it is proposed an identification algorithm of recursive least square parameter estimation online to distinguish the internal fault from external fault, which is based on that the winding resistance and short circuit reactance values have not been changed in normal operation for impedance matching balance transformer or external fault, but they have been changed for internal fault. The curves of simulation proved that the proposed algorithm could effectively distinguish internal fault from external fault. The research results show the feasibility to identify two sorts of different faults.

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Localization of turn-to-turn fault in transformers using Artificial Neural Networks and winding transfer function

Cited by 24 publications

References 3 publications

Transformer winding faults classification based on transfer function analysis by support vector machine

Transformer winding faults classification based on transfer function analysis by support vector machine

A probabilistic neural network classifier-based method for transformer winding fault identification through its transfer function measurement

Fault Diagnosis System Identification Based on Impedance Matching Balance Transformer

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