Adaptive Power Regulation Control for Floating Wind Turbines With Guaranteed Transient Performance

Floating offshore wind turbines (FOWTs) hold significant potential for developing wind energy in deep-sea areas. However, they are prone to additional motions, heavy workloads, and undesirable fluctuations under combined wind-wave loads. To tackle these issues, this paper presents a novel adaptive blade pitch control strategy with guaranteed transient performance for FOWTs operating in above-rated wind speed regions. This strategy effectively suppresses platform pitch motion to enhance system stability while maintaining the power output at its rated value. Specifically, this method initiates by defining a filtered regulation error to solve the non-affine nature of its model. An adaptive blade pitch controller is subsequently proposed, integrating an online learning approximator to cope with the unknown dynamics caused by unmeasurable external environmental inputs and model uncertainties. To further mitigate approximation errors, a nonlinear robust control law with adaptive gains is employed, enhancing the robustness and adaptive capabilities of the controller. Superior to the traditional adaptive controllers, a significant advantage of this strategy is its ability to quantify and ensure regulatory performance within predefined constraints during both transient and steady-state stages, thereby achieving high-performance control in power regulation. Finally, simulation studies are conducted using the OpenFAST software to show the capability of the presented blade pitch controller, which guarantees stable power generation with guaranteed transient performance.

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Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3568515
Citations
4
Primary Topic
Wave and Wind Energy Systems
Type
article
Field-Weighted Citation Impact
4.58

Funders

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article

Adaptive Power Regulation Control for Floating Wind Turbines With Guaranteed Transient Performance

Wenchao Meng, Qinmin Yang, Yingjie Gong, Hua Geng
4 citations
IEEE Transactions on Automation Science and Engineering
Wave and Wind Energy Systems
4.58
article

Adaptive Power Regulation Control for Floating Wind Turbines With Guaranteed Transient Performance

Wenchao Meng, Qinmin Yang, Yingjie Gong, Hua Geng
article en
4 citations

Abstract

Floating offshore wind turbines (FOWTs) hold significant potential for developing wind energy in deep-sea areas. However, they are prone to additional motions, heavy workloads, and undesirable fluctuations under combined wind-wave loads. To tackle these issues, this paper presents a novel adaptive blade pitch control strategy with guaranteed transient performance for FOWTs operating in above-rated wind speed regions. This strategy effectively suppresses platform pitch motion to enhance system stability while maintaining the power output at its rated value. Specifically, this method initiates by defining a filtered regulation error to solve the non-affine nature of its model. An adaptive blade pitch controller is subsequently proposed, integrating an online learning approximator to cope with the unknown dynamics caused by unmeasurable external environmental inputs and model uncertainties. To further mitigate approximation errors, a nonlinear robust control law with adaptive gains is employed, enhancing the robustness and adaptive capabilities of the controller. Superior to the traditional adaptive controllers, a significant advantage of this strategy is its ability to quantify and ensure regulatory performance within predefined constraints during both transient and steady-state stages, thereby achieving high-performance control in power regulation. Finally, simulation studies are conducted using the OpenFAST software to show the capability of the presented blade pitch controller, which guarantees stable power generation with guaranteed transient performance.

IEEE Transactions on Automation Science and EngineeringVol. 22
State Key Laboratory of Industrial Control Technology (CN), Tsinghua University (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 6%
Wave and Wind Energy Systems
4.58
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