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Comparison Study of Induction Motor Models Considering Iron Loss for Electric Drives

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  • Kang Wang

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China
    Qingdao VECCON Electric Co., Ltd., Qingdao 266200, China)

  • Ruituo Huai

    (College of Electrical Engineering and Automation, Shandong University of Science and Technology, Qingdao 266590, China)

  • Zhihao Yu

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Xiaoyang Zhang

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Fengjuan Li

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

  • Luwei Zhang

    (College of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, China)

Abstract

In a variety of motor models, the effects of iron-loss (ILS) on motor control accuracy and efficiency are generally ignored. This makes it difficult for the motor control system to obtain accurate control parameters (especially on high speed and low load conditions), and limits the improvement of motor control accuracy. This paper aims to clarify the influence of different ILS modeling and observation methods on motor control performance. Three equivalent models of motors with iron losses are compared. These models are: A parallel model, a series model and the simplified traditional model. Three tests are conducted to obtain the effect of ILS perturbation on ILS estimation results, and then to derive the sensitivity of the motor state and torque to the perturbation. These test conditions include: Ideal no-load, heavy-load, locked-rotor, and ILS perturbations during speed regulation. Simulation results show that the impedance and excitation characteristics of the series model and the parallel model are similar, and the traditional model has the best speed regulation smoothness. The ILS estimation errors of the series model is nearly constant and easy to compensate. For accurate ILS observation results, the series model can achieve better control accuracy.

Suggested Citation

  • Kang Wang & Ruituo Huai & Zhihao Yu & Xiaoyang Zhang & Fengjuan Li & Luwei Zhang, 2019. "Comparison Study of Induction Motor Models Considering Iron Loss for Electric Drives," Energies, MDPI, vol. 12(3), pages 1-13, February.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:3:p:503-:d:203604
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    References listed on IDEAS

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    Cited by:

    1. Chaymae Fahassa & Yassine Zahraoui & Mohammed Akherraz & Mohammed Kharrich & Ehab E. Elattar & Salah Kamel, 2022. "Induction Motor DTC Performance Improvement by Inserting Fuzzy Logic Controllers and Twelve-Sector Neural Network Switching Table," Mathematics, MDPI, vol. 10(9), pages 1-14, April.
    2. Elzbieta Szychta & Leszek Szychta, 2021. "Collective Losses of Low Power Cage Induction Motors—A New Approach," Energies, MDPI, vol. 14(6), pages 1-19, March.
    3. Maria Dems & Krzysztof Komeza & Jacek Szulakowski & Witold Kubiak, 2021. "Dynamic Simulation of High-Speed Induction Motor," Energies, MDPI, vol. 14(9), pages 1-14, May.
    4. Ondrej Lipcak & Filip Baum & Jan Bauer, 2021. "Influence of Selected Non-Ideal Aspects on Active and Reactive Power MRAS for Stator and Rotor Resistance Estimation," Energies, MDPI, vol. 14(20), pages 1-19, October.
    5. Giovanni Bucci & Fabrizio Ciancetta & Edoardo Fiorucci & Simone Mari & Maria Anna Segreto, 2019. "The Measurement of Additional Losses in Induction Motors: Discussion about the Actually Achievable Uncertainty," Energies, MDPI, vol. 13(1), pages 1-13, December.

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