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Electrical, Control and Communication Engineering

The Journal of Riga Technical University

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2255-9159
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Design Optimization with Geometric Programming for Core Type Large Power Transformers

Tamás Orosz / István Vajda
Published Online: 2014-10-23 | DOI: https://doi.org/10.2478/ecce-2014-0012

Abstract

A good transformer design satisfies certain functions and requirements. We can satisfy these requirements by various designs. The aim of the manufacturers is to find the most economic choice within the limitations imposed by the constraint functions, which are the combination of the design parameters resulting in the lowest cost unit. One of the earliest application of the Geometric Programming [GP] is the optimization of power transformers. The GP formalism has two main advantages. First the formalism guarantees that the obtained solution is the global minimum. Second the new solution methods can solve even large-scale GPs extremely efficiently and reliably. The design optimization program seeks a minimum capitalized cost solution by optimally setting the transformer's geometrical and electrical parameters. The transformer's capitalized cost chosen for object function, because it takes into consideration the manufacturing and the operational costs. This paper considers the optimization for three winding, three phase, core-form power transformers. This paper presents the implemented transformer cost optimization model and the optimization results.

Keywords: Design Optimization; Heuristic Algorithms; Mathematical programming; Power Engineering Computing; Power transformers

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About the article

* Address: Egry J. u. 18, H-1111 Budapest, Hungary

** Postal address: 1034 Budapest, Bécsi út 94-96. C II. 217


Published Online: 2014-10-23


Citation Information: Electrical, Control and Communication Engineering, ISSN (Online) 2255-9159, DOI: https://doi.org/10.2478/ecce-2014-0012.

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© 2014 Riga Technical University. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License. BY-NC-ND 3.0

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