Application of numerical techniques to FRA data for diagnosing integrity of transformer windings

The paper presents the investigations carried out on a transformer for various simulated faults to obtain FRA data and study the applicability of various numerical techniques. The numerical parameters Viz., Min-Max ratio (MM), Mean Square Error (MSE), Maximum Absolute Difference (MABS), Absolute Sum of Logarithmic Error (ASLE), Standard Deviation (SD) and Correlation Coefficient (CC) computed in three different frequency bands are presented to aid the interpretation of SFRA data. Frequency responses among identical phases of the sister units and different phases of a healthy three phase transformer were used to obtain the proposed representative numerical parameters for comparison. Deviation of the numerical parameters from the proposed representative parameters for different types of simulated faults is analyzed to diagnose the condition of the transformer. The analysis of the deviations in the parameters suggests the possibility of using the numerical approach to diagnose the transformer winding displacements and deformations. Interpretations of the results presented also indicate the possibility of discriminating the faulty winding using numerical parameter, computed from reference base data, symmetry of the windings, sister units and type of winding approach, and comparing them with the proposed representative numerical parameters presented in the paper.

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

[2]  L. Haydock,et al.  Swept frequency response test for condition monitoring of power transformer , 1995, Proceedings:Electrical Electronics Insulation Conference and Electrical Manufacturing & Coil Winding Conference.

[3]  J. Rickmann,et al.  A new technique to detect winding displacements in power transformers using frequency response analysis , 2003, 2003 IEEE Bologna Power Tech Conference Proceedings,.

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

[5]  Birgitte Bak-Jensen,et al.  Detection of faults and ageing phenomena in transformers by transfer functions , 1995 .

[6]  W H Tang,et al.  Detection of minor winding deformation fault in high frequency range for power transformer , 2010, IEEE PES General Meeting.

[7]  Robert V. Brill,et al.  Applied Statistics and Probability for Engineers , 2004, Technometrics.

[8]  E. Mombello,et al.  New methodology for diagnosing faults in power transformer windings through the Sweep Frequency Response Analysis (SFRA) , 2008, 2008 IEEE/PES Transmission and Distribution Conference and Exposition: Latin America.

[9]  Y. M. Li,et al.  Application of artificial neural network to the detection of the transformer winding deformation , 1999 .

[10]  Stephen B. Vardeman,et al.  Statistics for Engineering Problem Solving. , 1996 .

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

[12]  PooGyeon Park,et al.  Fault diagnosis of a power transformer using an improved frequency-response analysis , 2005 .

[13]  S. A. Ryder.  Methods for comparing frequency response analysis measurements , 2002, Conference Record of the the 2002 IEEE International Symposium on Electrical Insulation (Cat. No.02CH37316).

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