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Frequency dependent strategy for mitigating wind power fluctuations of a doubly-fed induction generator wind turbine based on virtual inertia control and blade pitch angle regulation

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  • Ochoa, Danny
  • Martinez, Sergio

Abstract

This paper presents a new approach to address the issue of the frequency deviations induced by the fluctuating power injected into the grid by doubly-fed induction generator based wind turbines in a weak or isolated power system that is based in a frequency dependent smoothing of the wind power. An algorithm that allows an improved maximum power point tracking curve shifting as a function of the blade pitch angle variation has been proposed, taking advantage of the combined effect of two frequency control strategies: one based on the use of the stored kinetic energy by means of the concept of virtual inertia, and the other, by limiting the captured wind power through blade pitch angle regulation. This algorithm has been devised to reduce the turbine deload requirements, and at the same time, to increase the available kinetic energy in the turbine. This results in a more effective fast-frequency response with a lower amount of non-dispatched wind energy. The effectiveness of the proposed method is assessed by considering a real wind profile in time-domain simulations.

Suggested Citation

  • Ochoa, Danny & Martinez, Sergio, 2018. "Frequency dependent strategy for mitigating wind power fluctuations of a doubly-fed induction generator wind turbine based on virtual inertia control and blade pitch angle regulation," Renewable Energy, Elsevier, vol. 128(PA), pages 108-124.
    • Handle: RePEc:eee:renene:v:128:y:2018:i:pa:p:108-124
    DOI: 10.1016/j.renene.2018.05.047
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    5. El-Bidairi, Kutaiba S. & Nguyen, Hung Duc & Mahmoud, Thair S. & Jayasinghe, S.D.G. & Guerrero, Josep M., 2020. "Optimal sizing of Battery Energy Storage Systems for dynamic frequency control in an islanded microgrid: A case study of Flinders Island, Australia," Energy, Elsevier, vol. 195(C).
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    7. Guglielmo D’Amico & Filippo Petroni & Salvatore Vergine, 2022. "Ramp Rate Limitation of Wind Power: An Overview," Energies, MDPI, vol. 15(16), pages 1-15, August.
    8. Ting-Hsuan Chien & Yu-Chuan Huang & Yuan-Yih Hsu, 2020. "Neural Network-Based Supplementary Frequency Controller for a DFIG Wind Farm," Energies, MDPI, vol. 13(20), pages 1-15, October.
    9. Wang, Huaizhi & Liu, Yangyang & Zhou, Bin & Voropai, Nikolai & Cao, Guangzhong & Jia, Youwei & Barakhtenko, Evgeny, 2020. "Advanced adaptive frequency support scheme for DFIG under cyber uncertainty," Renewable Energy, Elsevier, vol. 161(C), pages 98-109.
    10. Ana Fernández-Guillamón & Antonio Vigueras-Rodríguez & Emilio Gómez-Lázaro & Ángel Molina-García, 2018. "Fast Power Reserve Emulation Strategy for VSWT Supporting Frequency Control in Multi-Area Power Systems," Energies, MDPI, vol. 11(10), pages 1-20, October.
    11. Frate, G.F. & Cherubini, P. & Tacconelli, C. & Micangeli, A. & Ferrari, L. & Desideri, U., 2019. "Ramp rate abatement for wind power plants: A techno-economic analysis," Applied Energy, Elsevier, vol. 254(C).
    12. Deyi Fu & Lingxing Kong & Lice Gong & Anqing Wang & Haikun Jia & Na Zhao, 2023. "Wind Turbine Load Optimization Control Strategy Based on LIDAR Feed-Forward Control for Primary Frequency Modulation Process with Pitch Angle Reservation," Energies, MDPI, vol. 16(1), pages 1-14, January.
    13. Kheshti, Mostafa & Ding, Lei & Nayeripour, Majid & Wang, Xiaowei & Terzija, Vladimir, 2019. "Active power support of wind turbines for grid frequency events using a reliable power reference scheme," Renewable Energy, Elsevier, vol. 139(C), pages 1241-1254.
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