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Maximum Power Point Tracking Sensorless Control of an Axial-Flux Permanent Magnet Vernier Wind Power Generator

Author

Listed:
  • Xiang Luo

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China
    State Key Laboratory of Ocean Engineering, Shanghai Jiao Tong University, Shanghai 200240, China)

  • Shuangxia Niu

    (Department of Electrical Engineering, The Hong Kong Polytechnic University, Hong Kong, China)

Abstract

Recently, Vernier permanent magnet (VPM) machines, one special case of magnetic flux-modulated (MFM) machines, benefiting from their compact, simple construction and low-speed/ high-torque characteristics, have been receiving increasing interest. In this paper, the Vernier structure is integrated with an axial-flux PM machine to obtain the magnetic gear effect and produce an improved torque density for direct-drive wind power generation application. Another advantage of the proposed machine is that the stator flux rotating speed can be relatively high when the shaft speed is low. With this benefit, sensorless control strategy can be easily implemented in a wide speed range. In this paper, an improved sliding mode observer (SMO) is proposed to estimate the rotor position and the speed of the proposed machine. With the estimated shaft speeds, the maximum power point tracking (MPPT) control strategy is applied to maximize the wind power extraction. The machine design and the sensorless MPPT control strategy are verified by finite element analysis and experimental verification.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:9:y:2016:i:8:p:581-:d:74692
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    Citations

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    Cited by:

    1. Hina Usman & Junaid Ikram & Khurram Saleem Alimgeer & Muhammad Yousuf & Syed Sabir Hussain Bukhari & Jong-Suk Ro, 2021. "Analysis and Optimization of Axial Flux Permanent Magnet Machine for Cogging Torque Reduction," Mathematics, MDPI, vol. 9(15), pages 1-14, July.
    2. Ying Zhu & Ming Cheng & Haixiang Zang, 2017. "Sensorless Control for the EVT-Based New Dual Power Flow Wind Energy Conversion System," Energies, MDPI, vol. 10(7), pages 1-16, June.
    3. 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.
    4. Yubin Wang & Guangyong Yang & Xinkai Zhu & Xianglin Li & Wenzhong Ma, 2018. "Electromagnetic Characteristics Analysis of a High-Temperature Superconducting Field-Modulation Double-Stator Machine with Stationary Seal," Energies, MDPI, vol. 11(5), pages 1-13, May.
    5. Nikola Lopac & Neven Bulic & Niksa Vrkic, 2019. "Sliding Mode Observer-Based Load Angle Estimation for Salient-Pole Wound Rotor Synchronous Generators," Energies, MDPI, vol. 12(9), pages 1-22, April.
    6. Alireza Rasekh & Peter Sergeant & Jan Vierendeels, 2016. "Development of Correlations for Windage Power Losses Modeling in an Axial Flux Permanent Magnet Synchronous Machine with Geometrical Features of the Magnets," Energies, MDPI, vol. 9(12), pages 1-17, November.
    7. Xiaolian Zhang & Can Huang & Sipeng Hao & Fan Chen & Jingjing Zhai, 2016. "An Improved Adaptive-Torque-Gain MPPT Control for Direct-Driven PMSG Wind Turbines Considering Wind Farm Turbulences," Energies, MDPI, vol. 9(11), pages 1-16, November.
    8. Xianglin Li & K. T. Chau & Yubin Wang, 2016. "Modeling of a Field-Modulated Permanent-Magnet Machine," Energies, MDPI, vol. 9(12), pages 1-15, December.
    9. Dongran Song & Jian Yang & Mei Su & Anfeng Liu & Yao Liu & Young Hoon Joo, 2017. "A Comparison Study between Two MPPT Control Methods for a Large Variable-Speed Wind Turbine under Different Wind Speed Characteristics," Energies, MDPI, vol. 10(5), pages 1-18, May.

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