Transformer Top-Oil Temperature Modeling and Simulation
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.
[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.
[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.
@article{"International Journal of Information, Control and Computer Sciences:61020", author = "T. C. B. N. Assunção and J. L. Silvino and P. Resende", title = "Transformer Top-Oil Temperature Modeling and Simulation", abstract = "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.", volume = "2", number = "10", pages = "3502-6", }