Transformer Top-Oil Temperature Modeling and Simulation
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32799
Transformer Top-Oil Temperature Modeling and Simulation

Authors: T. C. B. N. Assunção, J. L. Silvino, P. Resende


The winding hot-spot temperature is one of the most critical parameters that affect the useful life of the power transformers. The winding hot-spot temperature can be calculated as function of the top-oil temperature that can estimated by using the ambient temperature and transformer loading measured data. This paper proposes the estimation of the top-oil temperature by using a method based on Least Squares Support Vector Machines approach. The estimated top-oil temperature is compared with measured data of a power transformer in operation. The results are also compared with methods based on the IEEE Standard C57.91-1995/2000 and Artificial Neural Networks. It is shown that the Least Squares Support Vector Machines approach presents better performance than the methods based in the IEEE Standard C57.91-1995/2000 and artificial neural networks.

Keywords: Artificial Neural Networks, Hot-spot Temperature, Least Squares Support Vector, Top-oil Temperature.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 2441


[1] J. A. Jardini, J. L. P. Brittes, L. C. Magrini, M. A. Bini, and J. Yasuoka, "Power transformer temperature evaluation for overloading conditions," IEEE Transactions on Power Delivery, vol. 20, no. 1, pp. 179-184, January 2005.
[2] IEEE, Guide for Loading Mineral-Oil-Immersed Transformers, June 2002.
[3] V. Galdi, L. Ippolito, A. Piccolo, and A. Vaccaro, "Neural diagnostic system for transformer thermal overload protection," IEE Proceedings Electric Power Applications, vol. 147, pp. 415-421, September 2000.
[4] W. H. Tang, K. Spurgeon, Q. H. Wu, and Z. Richardson, "Modeling equivalent thermal dynamics of power using genetic algorithms," in Proceedings of the IEEE, 2002, pp. 1396-1400.
[5] Q. He, J. Si, and D. J. Tylavsky, "Prediction of top-oil temperature for transformers using neural networks," IEEE Transactions on PowerDelivery, vol. 15, pp. 1205-1211, October 2000.
[6] K. Narendra and K. Parthasarathy, "Adaptative identification and control of dynamical systems using neural networks,," in Proceedings of the 28th IEEE Conference on Decision and Control, 1990, pp. 1737-1738.
[7] V. Vapnik, The Nature of Statistical Learning Theory, 1995.
[8] J. A. Suykens and J. Vandewalle, "Multiclass least squares support vector machines," in International Joint Conference on Neural Networks, 1999.
[9] T. V. Gestel, J. K. Suykens, D. Baestaens, A. Lambrechts, G. Lanckriet, B. Vandaele, B. D. Moor, and J. Vandewalle, "Financial time series prediction using least squares support vector machines within the evidence framework," IEEE Transactions on Neural Networks, vol. 12, no. 4, pp. 809- 821, 2001.
[10] D. Peterchuck and A. Pahwa, "Sensitivy of transformer's hottest-spot and equivalent aging to selected parameters," IEEE Transactions on Power Delivery, vol. 17, no. 4, pp. 996-1001, October 2002.
[11] D. J. Tylavsky, Q. He, G. A. McCulla, and J. R. Hunt, "Sources of error in substation distribution transformer dynamic thermal modeling," IEEE Transactions on Power Delivery, vol. 15, no. 1, pp. 178-185, 2000.
[12] H. Demuth, M. Beale, and M. Hagan, Neural Netwoork Toolbox User-s Guide for Use with Matlab.
[13] K. Pelckmans, J. Suykens, T. V. Gestel, J. D. Brabanter, B. Hamers, B. Moor, and J. Vanderwalle, LS-SVMlab Toolbox, Version 1.5, Katholieke Universiteit Leuven, Department of Electrical Engineering - ESATSCDSISTA, February 2003.