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A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed

Author

Listed:
  • Flavie Didier

    (Energy Department, FEMTO-ST Institute (UMR 6174), Université de Franche-Comté, UTBM, CNRS, 90010 Belfort, France)

  • Yong-Chao Liu

    (Energy Department, FEMTO-ST Institute (UMR 6174), Université de Franche-Comté, UTBM, CNRS, 90010 Belfort, France)

  • Salah Laghrouche

    (Energy Department, FEMTO-ST Institute (UMR 6174), Université de Franche-Comté, UTBM, CNRS, 90010 Belfort, France)

  • Daniel Depernet

    (Energy Department, FEMTO-ST Institute (UMR 6174), Université de Franche-Comté, UTBM, CNRS, 90010 Belfort, France)

Abstract

This paper presents a comprehensive review of advanced control methods specifically designed for floating offshore wind turbines (FOWTs) above the rated wind speed. Focusing on primary control objectives, including power regulation at rated values, platform pitch mitigation, and structural load reduction, this paper begins by outlining the requirements and challenges inherent in FOWT control systems. It delves into the fundamental aspects of the FOWT system control framework, thereby highlighting challenges, control objectives, and conventional methods derived from bottom-fixed wind turbines. Our review then categorizes advanced control methods above the rated wind speed into three distinct approaches: model-based control, data-driven model-based control, and data-driven model-free control. Each approach is examined in terms of its specific strengths and weaknesses in practical application. The insights provided in this review contribute to a deeper understanding of the dynamic landscape of control strategies for FOWTs, thus offering guidance for researchers and practitioners in the field.

Suggested Citation

  • Flavie Didier & Yong-Chao Liu & Salah Laghrouche & Daniel Depernet, 2024. "A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed," Energies, MDPI, vol. 17(10), pages 1-33, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2257-:d:1390316
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    References listed on IDEAS

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    1. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    2. Keighobadi, Jafar & Mohammadian KhalafAnsar, Hadi & Naseradinmousavi, Peiman, 2022. "Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine," Applied Energy, Elsevier, vol. 316(C).
    3. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
    4. Feng Yang & Qing-wang Song & Lei Wang & Shan Zuo & Sheng-shan Li, 2014. "Wind and Wave Disturbances Compensation to Floating Offshore Wind Turbine Using Improved Individual Pitch Control Based on Fuzzy Control Strategy," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-10, March.
    5. Sfetsos, A., 2002. "A novel approach for the forecasting of mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 27(2), pages 163-174.
    6. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    7. Wakui, Tetsuya & Nagamura, Atsushi & Yokoyama, Ryohei, 2021. "Stabilization of power output and platform motion of a floating offshore wind turbine-generator system using model predictive control based on previewed disturbances," Renewable Energy, Elsevier, vol. 173(C), pages 105-127.
    8. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    9. Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
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