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Adaptive Online Extraction Method of Slot Harmonics for Multiphase Induction Motor

Author

Listed:
  • Jie Yu

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

  • Youjun Zhang

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

  • Hongyuan Shen

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

  • Xiaoqin Zheng

    (College of Electrical Engineering, Qingdao University, Qingdao 266071, China)

Abstract

The accurate extraction and analysis of slot harmonics caused by slotting in an induction motor are important for the motor’s performance evaluation and state monitoring. However, the frequency distribution of rotor slot harmonics (RSHs) varies along with the operating states of the motor, such as motor speed and slip ratio, and the voltage and current signals of the motor only contain small-amplitude RSHs compared with other harmonics; both make it difficult to extract and analyze the RSHs accurately online. While offline extraction and filters with constant parameters are mainly utilized in available works, a novel adaptive extraction method for RSHs in a multiphase induction motor is proposed here to realize online RSH extraction under different speed and load conditions. In this paper, the RSHs in the multiphase induction motors are firstly modeled by using the magnetic potential permeability method, and the influence of a skewed rotor on RSHs is analyzed through a multisection method. Then, an adaptive extraction method of RSHs is proposed, which can effectively realize the online processing of RSHs of stator current. Finally, the experimental platform of a nine-phase induction motor has been used to verify the effectiveness of the proposed method under different speeds and load conditions, with a relative error of less than 1% in identifying the RSH frequency distribution.

Suggested Citation

  • Jie Yu & Youjun Zhang & Hongyuan Shen & Xiaoqin Zheng, 2022. "Adaptive Online Extraction Method of Slot Harmonics for Multiphase Induction Motor," Energies, MDPI, vol. 15(18), pages 1-14, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:18:p:6643-:d:912246
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    References listed on IDEAS

    as
    1. Mahamadou Negue Diarra & Yifan Yao & Zhaoxuan Li & Mouhamed Niasse & Yonggang Li & Haisen Zhao, 2022. "In-Situ Efficiency Estimation of Induction Motors Based on Quantum Particle Swarm Optimization-Trust Region Algorithm (QPSO-TRA)," Energies, MDPI, vol. 15(13), pages 1-15, July.
    2. Andrzej Bień & Tomasz Drabek & Dawid Kara & Tomasz Kołacz, 2022. "Reduction in the Cogging Torques in the DCEFSM Motor by Changing the Geometry of the Rotor Teeth," Energies, MDPI, vol. 15(7), pages 1-17, March.
    3. Elia Brescia & Donatello Costantino & Paolo Roberto Massenio & Vito Giuseppe Monopoli & Francesco Cupertino & Giuseppe Leonardo Cascella, 2021. "A Design Method for the Cogging Torque Minimization of Permanent Magnet Machines with a Segmented Stator Core Based on ANN Surrogate Models," Energies, MDPI, vol. 14(7), pages 1-26, March.
    4. Jin-Cheol Park & Soo-Hwan Park & Jae-Hyun Kim & Soo-Gyung Lee & Geun-Ho Lee & Myung-Seop Lim, 2021. "Diagnosis and Robust Design Optimization of SPMSM Considering Back EMF and Cogging Torque due to Static Eccentricity," Energies, MDPI, vol. 14(10), pages 1-19, May.
    5. Chung-Seong Lee & Hae-Joong Kim, 2022. "Harmonic Order Analysis of Cogging Torque for Interior Permanent Magnet Synchronous Motor Considering Manufacturing Disturbances," Energies, MDPI, vol. 15(7), pages 1-13, March.
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