IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i17p5537-d629237.html
   My bibliography  Save this article

Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection

Author

Listed:
  • Martin Nell

    (Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany)

  • Alexander Kubin

    (Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany)

  • Kay Hameyer

    (Institute of Electrical Machines—IEM, RWTH Aachen University, 52062 Aachen, Germany)

Abstract

Optimization methods are increasingly used for the design process of electrical machines. The quality of the optimization result and the necessary simulation effort depend on the optimization methods, machine models and optimization parameters used. This paper presents a multi-stage optimization environment for the design optimization of induction machines. It uses the strategies of simulated annealing, evolution strategy and pattern search. Artificial neural networks are used to reduce the solution effort of the optimization. The selection of the electromagnetic machine model is made in each optimization stage using a methodical model selection approach. The selection of the optimization parameters is realized by a methodical parameter selection approach. The optimization environment is applied on the basis of an optimization for the design of an electric traction machine using the example of an induction machine and its suitability for the design of a machine is verified by a comparison with a reference machine.

Suggested Citation

  • Martin Nell & Alexander Kubin & Kay Hameyer, 2021. "Multi-Stage Optimization of Induction Machines Using Methods for Model and Parameter Selection," Energies, MDPI, vol. 14(17), pages 1-24, September.
  • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5537-:d:629237
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/17/5537/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/14/17/5537/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gang Lei & Jianguo Zhu & Youguang Guo & Chengcheng Liu & Bo Ma, 2017. "A Review of Design Optimization Methods for Electrical Machines," Energies, MDPI, vol. 10(12), pages 1-31, November.
    2. Martin Nell & Alexander Kubin & Kay Hameyer, 2021. "Approach for the Model and Parameter Selection for the Calculation of Induction Machines," Energies, MDPI, vol. 14(18), pages 1-20, September.
    3. L. Ingber, 1989. "Very fast simulated re-annealing," Lester Ingber Papers 89vf, Lester Ingber.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ryszard Palka, 2022. "The Performance of Induction Machines," Energies, MDPI, vol. 15(9), pages 1-4, April.
    2. Martin Nell & Alexander Kubin & Kay Hameyer, 2021. "Approach for the Model and Parameter Selection for the Calculation of Induction Machines," Energies, MDPI, vol. 14(18), pages 1-20, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicolas Bernard & Linh Dang & Luc Moreau & Salvy Bourguet, 2022. "A Pre-Sizing Method for Salient Pole Synchronous Reluctance Machines with Loss Minimization Control for a Small Urban Electrical Vehicle Considering the Driving Cycle," Energies, MDPI, vol. 15(23), pages 1-19, December.
    2. L. Ingber, 2015. "Synergy among multiple scales of neocortical interactions," Lester Ingber Papers 15sc, Lester Ingber.
    3. L. Ingber & B. Rosen, 1992. "Genetic algorithms and very fast simulated reannealing: A comparison," Lester Ingber Papers 92ga, Lester Ingber.
    4. Sakata, Shinichi & White, Halbert, 2001. "S-estimation of nonlinear regression models with dependent and heterogeneous observations," Journal of Econometrics, Elsevier, vol. 103(1-2), pages 5-72, July.
    5. Amir Atiya & Steve Wall, 2009. "An analytic approximation of the likelihood function for the Heston model volatility estimation problem," Quantitative Finance, Taylor & Francis Journals, vol. 9(3), pages 289-296.
    6. Moriguchi, Kai & Ueki, Tatsuhito & Saito, Masashi, 2020. "Establishing optimal forest harvesting regulation with continuous approximation," Operations Research Perspectives, Elsevier, vol. 7(C).
    7. Chang-Yong Lee & Dongju Lee, 2014. "Determination of initial temperature in fast simulated annealing," Computational Optimization and Applications, Springer, vol. 58(2), pages 503-522, June.
    8. Gerber, Mathieu & Bornn, Luke, 2018. "Convergence results for a class of time-varying simulated annealing algorithms," Stochastic Processes and their Applications, Elsevier, vol. 128(4), pages 1073-1094.
    9. Preminger, Arie & Franck, Raphael, 2007. "Forecasting exchange rates: A robust regression approach," International Journal of Forecasting, Elsevier, vol. 23(1), pages 71-84.
    10. Liqin Wu & Hao Chen & Tingyue Yu & Chengzhi Sun & Lin Wang & Xuerong Ye & Guofu Zhai, 2023. "Robust Design Optimization of the Cogging Torque for a PMSM Based on Manufacturing Uncertainties Analysis and Approximate Modeling," Energies, MDPI, vol. 16(2), pages 1-24, January.
    11. L. Ingber, 2018. "Quantum Variables in Finance and Neuroscience," Lester Ingber Papers 18qv, Lester Ingber.
    12. Sha Lin & Xin-Jiang He, 2022. "Analytically Pricing European Options under a New Two-Factor Heston Model with Regime Switching," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1069-1085, March.
    13. L. Ingber & H. Fujio & M.F. Wehner, 1991. "Mathematical comparison of combat computer models to exercise data," Lester Ingber Papers 91mc, Lester Ingber.
    14. Ryszard Palka, 2022. "The Performance of Induction Machines," Energies, MDPI, vol. 15(9), pages 1-4, April.
    15. L. Ingber, 2022. "Quantum Variables in Finance," Lester Ingber Papers 22qv, Lester Ingber.
    16. Sebastian Berhausen & Tomasz Jarek, 2021. "Method of Limiting Shaft Voltages in AC Electric Machines," Energies, MDPI, vol. 14(11), pages 1-19, June.
    17. L. Ingber, 2018. "Model of Models (MOM)," Lester Ingber Papers 18mo, Lester Ingber.
    18. Chengcheng Liu & Jiawei Lu & Youhua Wang & Gang Lei & Jianguo Zhu & Youguang Guo, 2018. "Design Issues for Claw Pole Machines with Soft Magnetic Composite Cores," Energies, MDPI, vol. 11(8), pages 1-15, August.
    19. Md Sydur Rahman & Grace Firsta Lukman & Pham Trung Hieu & Kwang-Il Jeong & Jin-Woo Ahn, 2021. "Optimization and Characteristics Analysis of High Torque Density 12/8 Switched Reluctance Motor Using Metaheuristic Gray Wolf Optimization Algorithm," Energies, MDPI, vol. 14(7), pages 1-17, April.
    20. L. Ingber & D.D. Sworder, 1991. "Statistical mechanics of combat with human factors," Lester Ingber Papers 91ch, Lester Ingber.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5537-:d:629237. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.