Turn- to -turn fault localization of power transformers using neural network techniques

Firoozi, Hormatollah; Kharezi, M.

doi:10.1109/icpadm.2009.5252459

Cited by 21 publications

(11 citation statements)

References 3 publications

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“…The winding transfer functions at healthy and fault cases are compared and their difference is given as input to ANN model. Initially, ANN is trained for the various differences in the winding transfer functions and then, it acts as a decision making tool to identify fault in the winding [23]. Although theses methods detect the minute faults due to the impulse test, the neutral current is not readily accessible for a transformer and hence, these methods cannot be applied in that case.…”

Section: Advanced Methods (Ann and Wavelets)mentioning

confidence: 99%

Analysis of winding inter-turn fault in transformer: A review and transformer models

Bhide¹,

Srinivas²,

Banerjee³

et al. 2010

2010 IEEE International Conference on Sustainable Energy Technologies (ICSET)

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Transformers have emerged as an integrated part of a power system. Any fault in the transformer can cause a severe outage, which therefore necessitates continuous monitoring and diagnostics of its operation. The renewed thrust in smart power system networks along with the development of advanced methods in the monitoring and diagnostics has resulted in major impetus to research in the related domain. Among the detection of various faults in the transformer (or in any electrical machine in general), detection of winding inter-turn fault is critical since its effect is not easily comprehendible at lower magnitude in the signatures of terminal voltages and currents. Several techniques have been reported in the literature for detecting this fault. This paper reviews and compares the diagnostics methods based on their advantages and limitations. This highlights a further scope in the monitoring and diagnostic of the winding inter-turn fault. Finally, simple analytical models of three-and five-legged transformers are developed based on their electrical and magnetic equivalent circuits, which can be easily implemented in the analysis of winding inter-turn fault. Various results obtained from the analytical models are validated with the help of Finite Element (FE) modeling using ANSYS Parametric Design Language (APDL).

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Section: Advanced Methods (Ann and Wavelets)mentioning

confidence: 99%

Analysis of winding inter-turn fault in transformer: A review and transformer models

Bhide¹,

Srinivas²,

Banerjee³

et al. 2010

2010 IEEE International Conference on Sustainable Energy Technologies (ICSET)

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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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“…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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Turn- to -turn fault localization of power transformers using neural network techniques

Cited by 21 publications

References 3 publications

Analysis of winding inter-turn fault in transformer: A review and transformer models

Analysis of winding inter-turn fault in transformer: A review and transformer models

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

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