Abstract
This paper describes a new thermal model of oil-immersed, forced-air cooled power transformers and a methodology for model construction using intelligent learning applied to on-site measurements. The model delivers the value of bottom-oil and top-oil temperatures for thermal performance prediction and on-line monitoring of power transformers. The results obtained using the new thermal model are compared with the results of a traditional thermal model and the results derived from artificial neural networks.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
J. H. Provanzana, P. R. Gattens, Transformer condition monitoring realizing an intergrated adaptive analysis system, CIGRE 1992 session, 30 Agust–5 September.
T. Leibfried, W. Knorr, K. Viereck, On-line montoring of power transformers-trends, new development and first experiences, CIGRE 1998 session.
I. J. Kemp, Partial discharge plant-monitoring technology: Present and future developments, IEE Proceedings-Science, Measurement and Technology, vol.142, no.1, January 1995, pp.4–10.
James, L.et al, Model-based monitoring of transformers, Massachusetts Institute of Technology, Laboratory for Electromagnetic and Electronic Systems, 1995.
Transformers Committee of the IEEE Power Engineering Society, IEEE guide to loading mineral oil-immersed transformer, IEEE Std C57.115-1991, The Institute of Electrical and Electronics Engineers, Inc., 1991, 345 East 47th Street, New York, NY 10017, USA.
International electrotechnical commission, IEC354-loading guide for oil-immersed power transformers, equivalent to GB/T 15164-94 (Chinese Standard), 1991, Genève, Suisse.
Chinese standards board, GB1094-85, power transformers-Part 2: Temperature rise, 1985.
S. Q. Xu, Operation of power transformers, Chinese Electric Power Publisher, June 1993.
Q. H. Wu, J. T. Ma, Power system optimal reactive dispatch using evolutionary programming, IEEE Trans on Power Systems, vol.10, no.3, 1995, pp.1243–1249.
Q. H. Wu, Y. J. Cao, J. Y. Wen, Optimal reactive power dispatch using an adaptive genetic algorithm, International Journal of Electrical Power and Energy Systems, vol.20, no.8, 1998, pp.563–569.
J. T. Ma, Q. H. Wu, Generator parameter identification using evolutionary programming, International Journal of Electrical Power and Energy Systems, vol.17, no.6, 1995, pp.417–423.
P. Pillay, R. Nolan, Application of genetic algorithms to motor parameter determination for transient torque calculations, IEEE Transactions on Industry Applications, vol.33, no.5, September/October 1997, pp.1273–1282.
M. R. Zaman, Experimental testing of the artificial neural network based protection of power transformers, IEEE Transactions on Power Delivery, vol.13, no.2, Apirl 1998, pp.510–517.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tang, W.H., Zeng, H., Nuttall, K.I., Richardson, Z., Simonson, E., Wu, Q.H. (2000). Development of Power Transformer Thermal Models for Oil Temperature Prediction. In: Cagnoni, S. (eds) Real-World Applications of Evolutionary Computing. EvoWorkshops 2000. Lecture Notes in Computer Science, vol 1803. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45561-2_19
Download citation
DOI: https://doi.org/10.1007/3-540-45561-2_19
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-67353-8
Online ISBN: 978-3-540-45561-5
eBook Packages: Springer Book Archive