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

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Listed:
  • 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)

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.

Suggested Citation

  • Yan Li & Yifan Liu & Shasha Li & Leijie Qi & Jun Xie & Qing Xie, 2022. "A Novel Multi-Objective Optimal Design Method for Dry Iron Core Reactor by Incorporating NSGA-II, TOPSIS and Entropy Weight Method," Energies, MDPI, vol. 15(19), pages 1-15, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7344-:d:934778
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    References listed on IDEAS

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    1. Dhiman, Harsh S. & Deb, Dipankar, 2020. "Fuzzy TOPSIS and fuzzy COPRAS based multi-criteria decision making for hybrid wind farms," Energy, Elsevier, vol. 202(C).
    2. Mohamed El-Nemr & Mohamed Afifi & Hegazy Rezk & Mohamed Ibrahim, 2021. "Finite Element Based Overall Optimization of Switched Reluctance Motor Using Multi-Objective Genetic Algorithm (NSGA-II)," Mathematics, MDPI, vol. 9(5), pages 1-20, March.
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