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Grid-integrated permanent magnet synchronous generator based wind energy conversion systems: A technology review

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  • Tripathi, S.M.
  • Tiwari, A.N.
  • Singh, Deependra

Abstract

The growing trends in wind energy technology are motivating the researchers to work in this area with the aim towards the optimization of the energy extraction from the wind and the injection of the quality power into the grid. Over the last few years, wind generators based on permanent magnet synchronous machines (PMSMs) are becoming the most popular solution for the modern wind energy conversion systems (WECSs). This paper presents a concise review of the grid-integrated WECSs employing permanent magnet synchronous generators (PMSGs). It reviews the trends in converter topologies, control methodologies, and methods for maximum energy extraction in PMSG based WECSs, which have been reported in various research literatures primarily in reputed research journals and transactions during last few years. It also presents an overview to the grid interconnection issues related to output power smoothing and reactive power control in addition to fault-ride-through (FRT) and grid support capabilities of PMSG based WECSs. This review article will serve the researchers working in the area of grid-integrated PMSG based WECSs in the exploration of trends, developments and challenges in the past research works and in finding out the relevant references for their research work.

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  • Tripathi, S.M. & Tiwari, A.N. & Singh, Deependra, 2015. "Grid-integrated permanent magnet synchronous generator based wind energy conversion systems: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1288-1305.
    • Handle: RePEc:eee:rensus:v:51:y:2015:i:c:p:1288-1305
    DOI: 10.1016/j.rser.2015.06.060
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    Cited by:

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    10. Ayodele, T.R. & Ogunjuyigbe, A.S.O. & Adetokun, B.B., 2017. "Optimal capacitance selection for a wind-driven self-excited reluctance generator under varying wind speed and load conditions," Applied Energy, Elsevier, vol. 190(C), pages 339-353.
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    15. Zhicheng Lin & Song Zheng & Zhicheng Chen & Rong Zheng & Wang Zhang, 2019. "Application Research of the Parallel System Theory and the Data Engine Approach in Wind Energy Conversion System," Energies, MDPI, vol. 12(5), pages 1-20, March.
    16. Wajahat Ullah Khan Tareen & Muhammad Aamir & Saad Mekhilef & Mutsuo Nakaoka & Mehdi Seyedmahmoudian & Ben Horan & Mudasir Ahmed Memon & Nauman Anwar Baig, 2018. "Mitigation of Power Quality Issues Due to High Penetration of Renewable Energy Sources in Electric Grid Systems Using Three-Phase APF/STATCOM Technologies: A Review," Energies, MDPI, vol. 11(6), pages 1-41, June.
    17. Vasudevan, Krishnakumar R. & Ramachandaramurthy, Vigna K. & Venugopal, Gomathi & Ekanayake, J.B. & Tiong, S.K., 2021. "Variable speed pumped hydro storage: A review of converters, controls and energy management strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    18. Muhammad Maaruf & Md Shafiullah & Ali T. Al-Awami & Fahad S. Al-Ismail, 2021. "Adaptive Nonsingular Fast Terminal Sliding Mode Control for Maximum Power Point Tracking of a WECS-PMSG," Sustainability, MDPI, vol. 13(23), pages 1-19, December.
    19. Abrar Ahmed Chhipa & Vinod Kumar & Raghuveer Raj Joshi & Prasun Chakrabarti & Michal Jasinski & Alessandro Burgio & Zbigniew Leonowicz & Elzbieta Jasinska & Rajkumar Soni & Tulika Chakrabarti, 2021. "Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS," Energies, MDPI, vol. 14(19), pages 1-19, October.
    20. Fathabadi, Hassan, 2016. "Maximum mechanical power extraction from wind turbines using novel proposed high accuracy single-sensor-based maximum power point tracking technique," Energy, Elsevier, vol. 113(C), pages 1219-1230.
    21. Yang, Bo & Yu, Tao & Shu, Hongchun & Zhang, Yuming & Chen, Jian & Sang, Yiyan & Jiang, Lin, 2018. "Passivity-based sliding-mode control design for optimal power extraction of a PMSG based variable speed wind turbine," Renewable Energy, Elsevier, vol. 119(C), pages 577-589.
    22. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).

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