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Wave forecast and its application to the optimal control of offshore floating wind turbine for load mitigation

Author

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  • Ma, Yu
  • Sclavounos, Paul D.
  • Cross-Whiter, John
  • Arora, Dhiraj

Abstract

Control algorithms play an important role in energy capture and load mitigation for offshore floating wind turbines (OFWTs). One of the advanced and effective control techniques is the feedforward or model predictive control approach, which requires the forecast of incoming environment conditions. For OFWTs, wave loading is one of the dominant sources to excite structural responses. This study is thus motivated to develop forecasting algorithms for wave elevations and wave excitation forces with the purpose of applying feedforward controllers on OFWTs. Two forecasting algorithms, the approximate Prony Method based on ESPRIT (Estimation of Signal Parameters via Rotational Invariance Techniques) and SVM (Support Vector Machine) regression, are developed and validated using wave records from tank tests. Utilizing the forecasted wave elevations and wave excitation forces, a feedforward LQR controller is designed to mitigate structural loads of an OFWT system.

Suggested Citation

  • Ma, Yu & Sclavounos, Paul D. & Cross-Whiter, John & Arora, Dhiraj, 2018. "Wave forecast and its application to the optimal control of offshore floating wind turbine for load mitigation," Renewable Energy, Elsevier, vol. 128(PA), pages 163-176.
    • Handle: RePEc:eee:renene:v:128:y:2018:i:pa:p:163-176
    DOI: 10.1016/j.renene.2018.05.059
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    Citations

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    Cited by:

    1. Daniel Clemente & Felipe Teixeira-Duarte & Paulo Rosa-Santos & Francisco Taveira-Pinto, 2023. "Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource," Energies, MDPI, vol. 16(12), pages 1-28, June.
    2. Joannes Olondriz & Josu Jugo & Iker Elorza & Santiago Alonso-Quesada and Aron Pujana-Arrese, 2019. "A Feedback Control Loop Optimisation Methodology for Floating Offshore Wind Turbines," Energies, MDPI, vol. 12(18), pages 1-12, September.
    3. Zhang, Jincheng & Zhao, Xiaowei & Jin, Siya & Greaves, Deborah, 2022. "Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning," Applied Energy, Elsevier, vol. 324(C).
    4. 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.
    5. Liang, Yi & Niu, Dongxiao & Hong, Wei-Chiang, 2019. "Short term load forecasting based on feature extraction and improved general regression neural network model," Energy, Elsevier, vol. 166(C), pages 653-663.
    6. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei & Wang, Daming & Hann, Martyn & Greaves, Deborah, 2023. "Phase-resolved real-time forecasting of three-dimensional ocean waves via machine learning and wave tank experiments," Applied Energy, Elsevier, vol. 348(C).
    7. Shah, Kamran Ali & Meng, Fantai & Li, Ye & Nagamune, Ryozo & Zhou, Yarong & Ren, Zhengru & Jiang, Zhiyu, 2021. "A synthesis of feasible control methods for floating offshore wind turbine system dynamics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
    8. Liu, Yue & Zhang, Xiantao & Dong, Qing & Chen, Gang & Li, Xin, 2024. "Phase-resolved wave prediction with linear wave theory and physics-informed neural networks," Applied Energy, Elsevier, vol. 355(C).
    9. Zhang, Zhenquan & Qin, Jian & Wang, Dengshuai & Wang, Wei & Liu, Yanjun & Xue, Gang, 2023. "Research on wave excitation estimators for arrays of wave energy converters," Energy, Elsevier, vol. 264(C).
    10. Mahmoodi, Kumars & Nepomuceno, Erivelton & Razminia, Abolhassan, 2022. "Wave excitation force forecasting using neural networks," Energy, Elsevier, vol. 247(C).
    11. Doubrawa, Paula & Churchfield, Matthew J. & Godvik, Marte & Sirnivas, Senu, 2019. "Load response of a floating wind turbine to turbulent atmospheric flow," Applied Energy, Elsevier, vol. 242(C), pages 1588-1599.

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