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<p>Modern wind turbine design is evolving toward large-scale, high-capacity configurations. Under complex operational conditions, these turbines are subjected to significant mechanical loads coupled with power fluctuations. The pitch control system, a critical component of wind turbines, plays a pivotal role in regulating power output and alleviating load fluctuations. Given the limitations of conventional pitch control in adapting to wide-ranging wind speed variations and the need to balance power regulation with load mitigation objectives, this study proposes a pitch control framework based on nonlinear model predictive control. Using the National Renewable Energy Laboratory 5MW turbine as the research object, a reduced-order dynamic model is developed through mechanistic analysis and applied to the pitch control system design. Leveraging the OpenFAST and MATLAB/Simulink co-simulation platform, numerical validation is performed under diverse wind conditions. The results demonstrate that the controller adapts dynamically to the turbine’s real-time operating conditions and dynamic scenarios. The multi-objective optimization framework enhances power regulation performance while effectively suppressing tower fore-aft oscillations, thereby reducing tower mechanical loads. Compared to traditional strategies, the proposed framework achieves simultaneous optimization of power output and mechanical load mitigation.</p>

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Coordinated Power and Mechanical Loads Optimization Strategy of Wind Turbine Based on Model Predictive Control

  • 期刊论文

  • 发表日期：

    2025年6月14日

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  • Bo Wei,
  • Hao Hu,
  • Shuangao Wang

摘要

Modern wind turbine design is evolving toward large-scale, high-capacity configurations. Under complex operational conditions, these turbines are subjected to significant mechanical loads coupled with power fluctuations. The pitch control system, a critical component of wind turbines, plays a pivotal role in regulating power output and alleviating load fluctuations. Given the limitations of conventional pitch control in adapting to wide-ranging wind speed variations and the need to balance power regulation with load mitigation objectives, this study proposes a pitch control framework based on nonlinear model predictive control. Using the National Renewable Energy Laboratory 5MW turbine as the research object, a reduced-order dynamic model is developed through mechanistic analysis and applied to the pitch control system design. Leveraging the OpenFAST and MATLAB/Simulink co-simulation platform, numerical validation is performed under diverse wind conditions. The results demonstrate that the controller adapts dynamically to the turbine’s real-time operating conditions and dynamic scenarios. The multi-objective optimization framework enhances power regulation performance while effectively suppressing tower fore-aft oscillations, thereby reducing tower mechanical loads. Compared to traditional strategies, the proposed framework achieves simultaneous optimization of power output and mechanical load mitigation.