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Parameter Identification of Inverter-Fed Induction Motors: A Review

Author

Listed:
  • Jing Tang

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Yongheng Yang

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Frede Blaabjerg

    (Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark)

  • Jie Chen

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Lijun Diao

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China)

  • Zhigang Liu

    (School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
    Beijing Engineering Research Center for Electrical Rail Transit, Beijing 100044, China)

Abstract

Induction motor parameters are essential for high-performance control. However, motor parameters vary because of winding temperature rise, skin effect, and flux saturation. Mismatched parameters will consequently lead to motor performance degradation. To provide accurate motor parameters, in this paper, a comprehensive review of offline and online identification methods is presented. In the implementation of offline identification, either a DC voltage or single-phase AC voltage signal is injected to keep the induction motor standstill, and the corresponding identification algorithms are discussed in the paper. Moreover, the online parameter identification methods are illustrated, including the recursive least square, model reference adaptive system, DC and high-frequency AC voltage injection, and observer-based techniques, etc. Simulations on selected identification techniques applied to an example induction motor are presented to demonstrate their performance and exemplify the parameter identification methods.

Suggested Citation

  • Jing Tang & Yongheng Yang & Frede Blaabjerg & Jie Chen & Lijun Diao & Zhigang Liu, 2018. "Parameter Identification of Inverter-Fed Induction Motors: A Review," Energies, MDPI, vol. 11(9), pages 1-21, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2194-:d:165108
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    References listed on IDEAS

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    1. Markus Henke & Gerrit Narjes & Jan Hoffmann & Constantin Wohlers & Stefan Urbanek & Christian Heister & Jörn Steinbrink & Wolf-Rüdiger Canders & Bernd Ponick, 2018. "Challenges and Opportunities of Very Light High-Performance Electric Drives for Aviation," Energies, MDPI, vol. 11(2), pages 1-25, February.
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    Cited by:

    1. Mladen Vučković & Vladimir Popović & Djura Oros & Veran Vasić & Darko Marčetić, 2021. "Low Voltage Induction Motor Traction Drive Self-Commissioning Technique with the Advanced Measured Signal Processing Procedure," Energies, MDPI, vol. 14(6), pages 1-18, March.
    2. Mohan Krishna Srinivasan & Febin Daya John Lionel & Umashankar Subramaniam & Frede Blaabjerg & Rajvikram Madurai Elavarasan & G. M. Shafiullah & Irfan Khan & Sanjeevikumar Padmanaban, 2020. "Real-Time Processor-in-Loop Investigation of a Modified Non-Linear State Observer Using Sliding Modes for Speed Sensorless Induction Motor Drive in Electric Vehicles," Energies, MDPI, vol. 13(16), pages 1-22, August.
    3. 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.
    4. Martin Ćalasan & Mihailo Micev & Ziad M. Ali & Ahmed F. Zobaa & Shady H. E. Abdel Aleem, 2020. "Parameter Estimation of Induction Machine Single-Cage and Double-Cage Models Using a Hybrid Simulated Annealing–Evaporation Rate Water Cycle Algorithm," Mathematics, MDPI, vol. 8(6), pages 1-29, June.
    5. Carmenza Moreno Roa & Adolfo Andrés Jaramillo Matta & Juan David Bastidas Rodríguez, 2020. "Stochastic Search Technique with Variable Deterministic Constraints for the Estimation of Induction Motor Parameters," Energies, MDPI, vol. 13(1), pages 1-21, January.
    6. S. Usha & C. Subramani & Sanjeevikumar Padmanaban, 2019. "Neural Network-Based Model Reference Adaptive System for Torque Ripple Reduction in Sensorless Poly Phase Induction Motor Drive," Energies, MDPI, vol. 12(5), pages 1-25, March.

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