Application of Characteristic Impedance and Wavelet Coherence Technique to Discriminate Mechanical Defects of Transformer Winding

Abstract Frequency response analysis is commonly used as an effective diagnostic tool to detect mechanical defects within power transformers. The frequency response analysis result is affected by the test condition. This article presents a new method based on transmission line diagnostics (characteristic impedance) to discriminate between axial displacement and radial deformation in transformer winding. This method is independent of external circuit elements. To study this method, an appropriate model of the transformer winding in the frequency range of kHz–MHz is used, and different mechanical defects are analyzed. The mechanical defects of the transformer winding are simulated using ATP/EMTP in order to obtain current and voltage signals. The simulation results show the sensitivity and capability of the proposed method using wavelet coherence technique in condition monitoring analysis.

[1]  T. Leibfried,et al.  Monitoring of power transformers using the transfer function method , 1999 .

[2]  J.R. Marti,et al.  Field Validation Tests of the TLD Box for Online Power Transformer Winding Monitoring Systems , 2007, 2007 Canadian Conference on Electrical and Computer Engineering.

[3]  Gevork B. Gharehpetian,et al.  Application of Classifiers for On-line Monitoring of Transformer Winding Axial Displacement by Electromagnetic Non-destructive Testing , 2011 .

[4]  K. Feser,et al.  Procedures for detecting winding displacements in power transformers by the transfer function method , 2004, IEEE Transactions on Power Delivery.

[5]  L. Satish,et al.  Construction of Equivalent Circuit of a Single and Isolated Transformer Winding From FRA Data Using the ABC Algorithm , 2012, IEEE Transactions on Power Delivery.

[6]  S.M. Gubanski,et al.  Exploring possibilities for characterization of power transformer insulation by frequency response analysis (FRA) , 2006, IEEE Transactions on Power Delivery.

[7]  C. Bengtsson,et al.  Status and trends in transformer monitoring , 1996 .

[8]  Stefan Tenbohlen,et al.  Mathematical Comparison Methods to Assess Transfer Functions of Transformers to Detect Different Types of Mechanical Faults , 2010, IEEE Transactions on Power Delivery.

[9]  Pradeep M. Nirgude,et al.  Application of numerical evaluation techniques for interpreting frequency response measurements in power transformers , 2008 .

[10]  Gevork B. Gharehpetian,et al.  Estimation of simulated transfer function to discriminate axial displacement and radial deformation of transformer winding , 2012 .

[11]  Eilert Bjerkan.  High frequency modeling of power transformers: Stresses and Diagnostics , 2005 .

[12]  G. B. Gharehpetian,et al.  A Vector-based Approach to Discriminate Radial Deformation and Axial Displacement of Transformer Winding and Determine Defect Extent , 2012 .

[13]  Caisheng Wang,et al.  A Hybrid Model of Transformer Windings for Very Fast Transient Analysis Based on Quasi-stationary Electromagnetic Fields , 2008 .

[14]  K. Ragavan,et al.  Localization of Changes in a Model Winding Based on Terminal Measurements: Experimental Study , 2007, IEEE Transactions on Power Delivery.

[15]  Jose R. Marti,et al.  A comparison of trans-admittance and characteristic impedance as metrics for detection of winding displacements in power transformers , 2009 .

[16]  Asghar Akbari,et al.  Localization of turn-to-turn fault in transformers using Artificial Neural Networks and winding transfer function , 2010, 2010 10th IEEE International Conference on Solid Dielectrics.

[17]  A.J. Vandermaar,et al.  Improved detection of power transformer winding movement by extending the FRA high frequency range , 2005, IEEE Transactions on Power Delivery.

[18]  Zhongdong Wang,et al.  Interpretation of Transformer FRA Responses— Part II: Influence of Transformer Structure , 2010, IEEE Transactions on Power Delivery.

[19]  Aslak Grinsted,et al.  Nonlinear Processes in Geophysics Application of the Cross Wavelet Transform and Wavelet Coherence to Geophysical Time Series , 2022 .

[20]  K. Feser,et al.  Transfer Function Method to Diagnose Axial Displacement and Radial Deformation of Transformer Winding , 2002, IEEE Power Engineering Review.

[21]  S. A. Ryder,et al.  Diagnosing transformer faults using frequency response analysis , 2003 .

[22]  Enrique E. Mombello,et al.  Sweep frequency response analysis (SFRA) for the assessment of winding displacements and deformation in power transformers , 2008 .

[23]  Gevork B. Gharehpetian,et al.  Localization of winding radial deformation and determination of deformation extent using vector fitting‐based estimated transfer function , 2009 .

[24]  Mehdi Vakilian,et al.  Transformer winding faults classification based on transfer function analysis by support vector machine , 2012 .

[25]  Sheng-wei Fei,et al.  Fault diagnosis of power transformer based on support vector machine with genetic algorithm , 2009, Expert Syst. Appl..