Design optimization of Rectifier Transformers
Date Issued
2020-05-08
Author(s)
Salkoski, Rasim
Chorbev, Ivan
Abstract
Optimization refers to finding one or more feasible solutions, which correspond to extreme values of one or more objectives. The need for finding such optimal solutions in a problem comes mostly from the extreme purpose of either designing a solution for minimum possible cost of fabrication, or for maximum possible reliability, or others. Because of such extreme properties of optimal solutions, optimization methods are of great importance in practice, particularly in engineering
design, scientific experiments and business decision-making. Rectifier transformers deserve extensive treatment in the field of research and production, due to the fact that the electric energy undergoes several transformations on its way from generators to the consumers i.e. rectifiers. In this paper, an effective application of the population based search Differential Evolution algorithm is proposed with the aim of minimizing the cost of the active part of wound core rectifier transformers. The constraints resulting from international specifications and customer needs are taken into account. The Objective Function that is optimized is a minimization dependent on multiple input variables. All constraints are normalized and modeled as inequalities.
design, scientific experiments and business decision-making. Rectifier transformers deserve extensive treatment in the field of research and production, due to the fact that the electric energy undergoes several transformations on its way from generators to the consumers i.e. rectifiers. In this paper, an effective application of the population based search Differential Evolution algorithm is proposed with the aim of minimizing the cost of the active part of wound core rectifier transformers. The constraints resulting from international specifications and customer needs are taken into account. The Objective Function that is optimized is a minimization dependent on multiple input variables. All constraints are normalized and modeled as inequalities.
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