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Transformer Design Optimization

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Spotlight on Modern Transformer Design

Part of the book series: Power Systems ((POWSYS))

Abstract

This chapter deals with modern design optimization of wound core type transformers. Four methods are presented that solve important transformer design problems. First, genetic algorithms are combined with artificial neural networks to optimally group 4 ∙ N available individual cores into N transformers so as to minimize the total no-load loss of N transformers. This method significantly reduces the no-load loss design margin as well as the cost of transformer main materials. Second, decision trees and artificial neural networks successfully solve the winding material selection problem, thus avoiding the need to optimize the transformer twice, once with copper and once with aluminum windings. Third, a mixed integer programming–finite element method (MIP-FEM) technique is developed for the solution of the transformer design optimization (TDO) problem. Finally, a recursive genetic algorithm–finite element method technique is developed to solve the TDO problem and is compared with MIP-FEM. The recursive genetic algorithm approach can be also very useful for the solution of other optimization problems in electric machines and power systems.

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© 2009 Springer-Verlag London Limited

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(2009). Transformer Design Optimization. In: Spotlight on Modern Transformer Design. Power Systems. Springer, London. https://doi.org/10.1007/978-1-84882-667-0_7

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  • DOI: https://doi.org/10.1007/978-1-84882-667-0_7

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-84882-666-3

  • Online ISBN: 978-1-84882-667-0

  • eBook Packages: EngineeringEngineering (R0)

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