Abstract
Power transformers have a key role in the power system grids. Their manufacturing and design must consider several aspects, such as technical limits, legal constrains, security constrains and manufacturing price. Considering only power transformers’ active parts, it is possible to identify 20 manufacturing specific parameters, and in economic point of view, 13 variables are also considered. Using a classic approach, variables are chosen accordingly with the defined constraints, followed by a sensitivity analysis preformed to each variable, to optimize the manufacturing cost. This procedure can be time consuming and the optimum may not be reached. In this paper, genetic algorithms are used. An innovative approach through the introduction of genetic compensation concept in mutation operator is detailed. Results pointed out an increased performance and consistency when compared with the classical approach.
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
Similar content being viewed by others
References
Mehta, R.M.P.H.D.: A review on transformer design optimization and performance analysis using artificial intelligence techniques. Int. J. Sci. Res. 3(9), 726–733 (2014)
Amoiralis, E.I., Tsili, M.A., Georgilakis, P.S.: The state of the art in engineering methods for transformer design and optimization: a survey. J. Optoelectron. Adv. Mater. 10(5), 1149–1158 (2008)
Aghmasheh, R., Rashtchi, V., Rahimpour, E.: Gray box modeling of power transformer windings based on design geometry and particle swarm optimization algorithm. IEEE Trans. Power Deliv. 33(5), 3284–3293 (2018)
Folorunso, O., Oriaifo, P.A., Idiagi, N.S., Ogujor, E.A.: Application of anto colony ptimization in distribution transformer sizing. Niger. J. Technol. (NIJOTECH) 36(4), 1233–1238 (2017)
Fouzai, M., Zouaghi, T.: Ant colony algorithm applied to power transformer optimization. In: International Conference on Electrical Engineering and Software Applications, pp. 1–5 (2013)
Finocchio, M.A.F., Lopes, J.J., de França, J.A., Piai, J.C., Mangili, Jr. J.F.: Neural networks applied to the design of dry type transformers: an example to analyze the winding temperature and elevate the thermal quality. Int. Trans. Electr. Energ. Syst. 27(e2257), 1–10 (2017)
Adly, A.A.: Computation of inrush current forces on transformer windings. IEEE Trans. Magnet. 37(4), 2855–2857 (2001)
Amoiralis, E.I., Georgilakis, P.S., Tsili, M.A., Kladas, A.G.: Artificial intelligence combined with hybrid FEM-BE techniques for global transformer optimization. IEEE Trans. Magnet. 43(4), 1633–1636 (2007)
Geromel, L.H., Souza, C.R.: The application on intelligent systems in power transformer design. In: IEEE Canadian Conference on Electric & Computer Engineering, pp. 285–290 (2002)
Mohammed, S.M., Vural, R.A.: NSGA-II + FEM based loss optimization of three-phase transformer. IEEE Trans. Indus. Electron. 66(9), 7417–7425 (2019)
Elia, S., Fabbri, G., Nisticò, E., Santini, E.: Design of cast-resin distribution transformers by means of genetic algorithms. In: International Symposium on Power Electronics, Electrical Drives, Automation and Motion, pp. 1473–1477 (2006)
Zhang, S., Hu, Q., Wang, X., Wang, D.: Research of transformer optimal design modeling and intelligent algorithm. Chin. Control Decis. Conf. 3, 213–218 (2011)
Yang, S., Jianrong, L., Xueliang, F., Haifang, W., Honghui, L.: Application research based on improved genetic algorithm in cloud task scheduling. J. Intell. Fuzzy Syst. 38(1), 239–246 (2020)
Georgilaki, P.S.: Spotlight on Modern Transformer Design. Springer, vol. 53, no. 9 (2009)
Goldberg, D.E., Deb, K.: A comparative analysis of selection schemes used in genetic algorithms. In: Foundations of Genetic Algorithms, vol. 1, pp. 69–93 (1991)
Coello, C.A.C., Montes, E.M.: Constraint-handling in genetic algorithms through the use of dominance-based tournament selection. Adv. Eng. Inf. 16(3), 193–203 (2002)
Tate, D.M., Smith, A.E.: Expected allele coverage and the role of mutation. In: 5th International Conference on Genetic Algorithms, pp. 31–37, USA (1993)
Grether, G.F.: Environmental change, phenotypic plasticity, and genetic compensation. In: The American Naturalist, vol. 166, no. 4 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 IFIP International Federation for Information Processing
About this paper
Cite this paper
Alves, P., Fonte, P.M., Pereira, R. (2021). Power Transformer Design Resorting to Metaheuristics Techniques. In: Camarinha-Matos, L.M., Ferreira, P., Brito, G. (eds) Technological Innovation for Applied AI Systems. DoCEIS 2021. IFIP Advances in Information and Communication Technology, vol 626. Springer, Cham. https://doi.org/10.1007/978-3-030-78288-7_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-78288-7_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-78287-0
Online ISBN: 978-3-030-78288-7
eBook Packages: Computer ScienceComputer Science (R0)
