Transfer Function Analysis to Evaluate Drying Quality of Power Transformers by Support Vector Machine
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Published:
Apr 5, 2013
Keywords:
Transformer Drying Process Transfer Function Support Vector Machine
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Abstract
For many years, an increasing interest existed in application of transfer functions (TF) method as a measure for detection of winding mechanical faults in transformers. However, this paper aims to change the application of TF method in order to evaluation of drying quality of power transformers during manufacturing process. For this purpose, support vector machine (SVM) is used. The required data for training and testing of SVM are carried out on 50MVA 132KV/33KV power transformer when the active part is placed in the drying chamber. Three dierent features extracted from the measured TFs are then used as the inputs to SVM to give an estimate for required time in drying process. The accuracy of proposed method is compared with the existing work in this eld. This comparison shows the superior capabilities of this proposed method.
Article Details
How to Cite
Bigdeli, M., & Firoozi, H. (2013). Transfer Function Analysis to Evaluate Drying Quality of Power Transformers by Support Vector Machine. ECTI Transactions on Electrical Engineering, Electronics, and Communications, 11(2), 8–15. https://doi.org/10.37936/ecti-eec.2013112.170627
Section
Electrical Power Systems
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References
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[23] A. Shintemirov, W. H. Tang, and Q. H. Wu, "Power transformer fault classication based on dissolved gas analysis by implementing bootstrap and genetic programming," IEEE Trans. Syst., Man, Cybernetics, vol. 39, no. 1, pp. 69-79, Jan. 2009.
[24] B. Gustavsen, and A. Semlyen, "Rational approximation of frequency domain responses by vector fitting," IEEE Trans. Power Del., vol. 14, iss. 3, pp. 1052-1061, Jul. 1999.
[25] V. Vapnik, "The nature of statistical learning theory,"Springer Verlag, New York, 1995.
[26] V. Vapnik, "Statistical learning theory," Wiley, New York, 1998.
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[28] A. Akbari, H. Firoozi, and M. Kharezi, "Investigations on sensitivity of frequency response analysis technique to measuring setup," Proc. the 15th Int. Symp. High Voltage Eng., 2007, pp. 496-499.
[29] H. Demuth, H. M. Beale, and M. Hagan, "Neural Network Toolbox for use with MATLAB," User's Guide Version 5, The MathWorks, Inc. 2006.
[2] M. Bigdeli, M. Vakilian, and E. Rahimpour, "A new method for detection and evaluation of winding mechanical faults in transformer through transfer function measurements," Advances Elect. Comput. Eng., vol. 11, no. 2, pp. 23-30, 2011.
[3] H. Firoozi, and S. Karami, "Experimental attempts and eld experiences to fault diagnosis of power transformers using FRA technique," Int. Review Elect. Eng., vol. 6, iss. 5, pp. 2221-2228, Sept. 2011.
[4] T. Leibfried, and K. Feser, "Monitoring of power transformers using the transfer function method," IEEE Trans. Power Del., vol. 14, no. 4, pp. 1333-1341, Oct. 1999.
[5] E. Rahimpour, M. Jabbari, and S. Tenbohlen, "Mathematical comparison methods to assess transfer functions of transformers to detect different types of mechanical faults," IEEE Trans. Power Del., vol. 25, iss. 4, pp. 2544-2555, Oct. 2010.
[6] M. Bigdeli, M. Vakilian, and E. Rahimpour, "Transformer winding faults classication based on transfer function analysis by support vector machine," IET Elect. Power Applicat., vol. 6, iss. 5, pp. 268-276, May 2012.
[7] M. Bigdeli, M. Vakilian, E. Rahimpour, and D. Azizian, "Theoretical and experimental investigation of transformer winding fault detection using comparison of transfer function coecients," ECTI Trans. Elect. Eng., Electron., Commun., vol. 10, no. 1, Apr. 2012.
[8] P. Karimifard, G. B. Gharehpetian, and S. Tenbohlen, "Determination of axial displacement extent based on transformer winding transfer function estimation using vector-tting method," European Trans. Elect. Power, vol. 18, pp. 423-436, May 2008.
[9] M. Wang, A. J. Vandermaar, and K. D. Srivastava, "Improved detection of power transformer winding movement by extending the FRA high frequency range," IEEE Trans. Power Del., vol. 20, no. 3, pp. 1930-1938, Jul. 2005.
[10] A. Akbari, H. Firoozi, H. Borsi, and M. Kharezi, "Assesment of drying quality for power transformers during manufacturing process using variation of transfer function," Proc. IEEE/CEIDP Conf., 2006, pp. 113-116.
[11] S. A. Ryder, "Diagnosing transformer faults using frequency response analysis," IEEE Elect. Insulation Mag., vol. 19, iss. 2, pp. 16-22, Mar.- Apr. 2003.
[12] S. A. Ryder, "Diagnosing a wide range of transformer faults using frequency response analysis," Proc. 13th Int. Symp. High Voltage Eng., 2003, pp. 323-327.
[13] J. R. Secue, and E. Mombello, "Sweep frequency response analysis (SFRA) for the assessment of winding displacements and deformation in power transformers,", Elect. Power Syst. Research, vol. 78, iss. 6, pp. 1119-1128, Jun. 2008.
[14] K. Pourhossein, G. B. Gharehpetian, E. Rahimpour and B. N. Araabi, "A probabilistic feature to determine type and extent of winding mechanical defects in power transformers," Elect. Power Syst. Research, vol. 82, no. 1, pp. 1-10, Jam. 2012.
[15] K. Pourhossein, G. B. Gharehpetian, E. Rahimpour, and B. N. Araabi, "A vector-based approach to discriminate radial deformation and axial displacement of transformer winding and determine defect extent," Elect. Power Components Syst., vol. 40, no. 6, pp. 597-612, Mar. 2012.
[16] K. Ragavan, and L. Satish, "Localization of changes in a model winding based on terminal measurements: experimental study," IEEE Trans. Power Del., vol. 22, no. 3, pp. 1557-1565, Jul. 2007.
[17] L. Satish, and S. K. Sahoo, "Locating faults in a transformer winding: an experimental study," Elect. Power Syst. Research, vol. 79, no. 1, pp. 89-97, Jan. 2009.
[18] K. Ragavan, and L. Satish, "Construction of physically realizable driving-point function from measured frequency response data on a model winding," IEEE Trans. Power Del., vol. 23, no. 2, pp. 760-767, Apr. 2008.
[19] E. Rahimpour, and S. Tenbohlen, "Experimental and theoretical investigation of disc space variation in real high-voltage windings using transfer function method," IET Elect. Power Applicat., vol. 4, iss. 6, pp. 451-461, 2010.
[20] M. Bigdeli, M. Vakilian, and E. Rahimpour, "Comparison of transfer functions using estimated rational functions to detect winding mechanical faults in transformers," Archives Elect. Eng., vol. 61, pp. 85-99, Mar. 2012.
[21] S. Birlasekaran, Y. Xingzhou, F. Fetherstone, R. Abell, and R. Middleton, "Diagnosis and identification of transformer faults from frequency response data," Proc. IEEE Conf. Power Eng. Soc. Winter Meeting, 2000, pp. 2251-2256.
[22] S. W. Fei, and X. B. Zhang, "Fault diagnosis of power transformer based on support vector machine with genetic algorithm," Expert Syst. Applicat., vol. 36, iss. 8, pp. 11352-11357, 2009.
[23] A. Shintemirov, W. H. Tang, and Q. H. Wu, "Power transformer fault classication based on dissolved gas analysis by implementing bootstrap and genetic programming," IEEE Trans. Syst., Man, Cybernetics, vol. 39, no. 1, pp. 69-79, Jan. 2009.
[24] B. Gustavsen, and A. Semlyen, "Rational approximation of frequency domain responses by vector fitting," IEEE Trans. Power Del., vol. 14, iss. 3, pp. 1052-1061, Jul. 1999.
[25] V. Vapnik, "The nature of statistical learning theory,"Springer Verlag, New York, 1995.
[26] V. Vapnik, "Statistical learning theory," Wiley, New York, 1998.
[27] C. W. Hsu, and C. J. Lin, "A comparison of methods for multiclass support vector machines," IEEE Trans. Neural Netw., vol. 13, no. 2, pp. 415-425, Mar. 2002.
[28] A. Akbari, H. Firoozi, and M. Kharezi, "Investigations on sensitivity of frequency response analysis technique to measuring setup," Proc. the 15th Int. Symp. High Voltage Eng., 2007, pp. 496-499.
[29] H. Demuth, H. M. Beale, and M. Hagan, "Neural Network Toolbox for use with MATLAB," User's Guide Version 5, The MathWorks, Inc. 2006.