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Comparative Evaluation of a Permanent Magnet Machine Saliency-Based Drive with Sine-Wave and Square-Wave Voltage Injection

Author

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  • Jyun-You Chen

    (Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan)

  • Shih-Chin Yang

    (Department of Mechanical Engineering, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan)

  • Kai-Hsiang Tu

    (Hua-Chuang Automobile Information Technical Center, Zhongxing Road, New Taipei 231, Taiwan)

Abstract

This paper improves a permanent magnet (PM) machine saliency-based drive performance based on the selection of a suitable injection signal. For the saliency-based position estimation, a persistently high-frequency (HF) voltage signal is injected to obtain a measurable spatial saliency feedback signal. The injection signal can be sine-wave or square-wave alternating current (AC) voltage manipulated by the inverter’s pulse width modulation (PWM). Due to the PWM dead-time effect, these HF voltage injection signals might be distorted, leading to secondary harmonics on the saliency signal. In addition, the flux saturation in machine rotors also results in other saliency harmonics. These nonlinear attributes cause position estimation errors on saliency-based drives. In this paper, two different voltage signals are analyzed to find a suited voltage which is less sensitive to these nonlinear attributes. Considering the inverter dead-time, a sine-wave voltage signal reduces its influence on the saliency signal. By contrast, the flux saturation causes the same amount of error on two injection signals. Analytical equations are developed to investigate position errors caused by the dead-time and flux saturation. An interior PM machine with the saliency ratio of 1.41 is tested for the experimental verification.

Suggested Citation

  • Jyun-You Chen & Shih-Chin Yang & Kai-Hsiang Tu, 2018. "Comparative Evaluation of a Permanent Magnet Machine Saliency-Based Drive with Sine-Wave and Square-Wave Voltage Injection," Energies, MDPI, vol. 11(9), pages 1-15, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2189-:d:165030
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    References listed on IDEAS

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    1. Ming-Shyan Wang & Tse-Ming Tsai, 2017. "Sliding Mode and Neural Network Control of Sensorless PMSM Controlled System for Power Consumption and Performance Improvement," Energies, MDPI, vol. 10(11), pages 1-15, November.
    2. Wei Hua & Ling Kang Zhou, 2015. "Investigation of a Co-Axial Dual-Mechanical Ports Flux-Switching Permanent Magnet Machine for Hybrid Electric Vehicles," Energies, MDPI, vol. 8(12), pages 1-19, December.
    3. Xin Qiu & Weiye Wang & Jianfei Yang & Jie Jiang & Jiquan Yang, 2017. "Phase-Inductance-Based Position Estimation Method for Interior Permanent Magnet Synchronous Motors," Energies, MDPI, vol. 10(12), pages 1-14, December.
    4. Xiang Luo & Shuangxia Niu, 2016. "Maximum Power Point Tracking Sensorless Control of an Axial-Flux Permanent Magnet Vernier Wind Power Generator," Energies, MDPI, vol. 9(8), pages 1-17, July.
    5. Younghoon Cho, 2016. "Improved Sensorless Control of Interior Permanent Magnet Sensorless Motors Using an Active Damping Control Strategy," Energies, MDPI, vol. 9(3), pages 1-15, February.
    6. Weiwei Gu & Xiaoyong Zhu & Li Quan & Yi Du, 2015. "Design and Optimization of Permanent Magnet Brushless Machines for Electric Vehicle Applications," Energies, MDPI, vol. 8(12), pages 1-13, December.
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    Cited by:

    1. Junlei Chen & Shuo Chen & Xiang Wu & Guojun Tan & Jianqi Hao, 2019. "A Super-Twisting Sliding-Mode Stator Flux Observer for Sensorless Direct Torque and Flux Control of IPMSM," Energies, MDPI, vol. 12(13), pages 1-17, July.
    2. Jongwon Choi, 2021. "Regression Model-Based Flux Observer for IPMSM Sensorless Control with Wide Speed Range," Energies, MDPI, vol. 14(19), pages 1-18, October.
    3. Baochao Wang & Yangrui Wang & Liguo Feng & Shanlin Jiang & Qian Wang & Jianhui Hu, 2019. "Permanent-Magnet Synchronous Motor Sensorless Control Using Proportional-Integral Linear Observer with Virtual Variables: A Comparative Study with a Sliding Mode Observer," Energies, MDPI, vol. 12(5), pages 1-12, March.

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