A Novel Multi-Objective Optimal Design Method for Dry Iron Core Reactor by Incorporating NSGA-II, TOPSIS and Entropy Weight Method

Yan Li, Yifan Liu, Shasha Li, Leijie Qi, Jun Xie () and Qing Xie
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Yan Li: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Yifan Liu: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Shasha Li: State Grid Hebei Baoding Electric Power Company Limited, Baoding 071051, China
Leijie Qi: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Jun Xie: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Qing Xie: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2022, vol. 15, issue 19, 1-15

Abstract: Dry iron core reactors are widely used in various power quality applications. Manufacturers want to optimize the cost and loss simultaneously, which is normally achieved by the designers’ experience. This approach is highly subjective and can lead to a non-ideal product. Thus, an objective dry iron core reactor design approach to balance the cost and loss with a scientific basis is desired. In this paper, a multi-objective optimal design method is proposed to optimize both the cost and loss of the reactor, which provides an automatic and scientific design method. Specifically, a three-dimensional finite element model of dry iron core reactor is established, based on which the dependency of cost and loss upon the wire size of the reactor’s winding is studied by using joint Matlab-finite element method (FEM) simulation. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to search for the Pareto optimal solution set, out of which the optimal wire size of the reactor is determined by using the fusion of the technique for order preference by similarity to ideal solution (TOPSIS) method and the entropy weight method. TOPSIS helps the designer to balance the concern between cost and loss, while the entropy weight method can determine the weight information through the dispersion degree of cost and loss. This methodology can avoid personal random subjective opinion when selecting the design solution out of the Pareto set. The calculation shows that the cost and loss can be reduced by up to 17.85% and 19.45%, respectively, with the proposed method. Furthermore, the obtained optimal design is approved by experimental tests.

Keywords: dry iron core reactor; multi-objective optimization; NSGA-II; matlab-finite element; TOPSIS; entropy weight method (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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