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<p>As the offshore wind energy sector advances into deeper, remote sea areas with escalating single-turbine power, the capacity of large floating wind turbines has expanded, heightening demands for control system precision. Traditional static loss model-based strategies struggle to meet dynamic operational needs in complex marine settings. This paper introduces a collaborative control method leveraging adaptive modeling of whole-machine dynamic losses. By developing a multi-physics coupling model and integrating real-time condition sensing with adaptive algorithms, it enables dynamic monitoring and active regulation of global losses. This approach combines multi-dimensional parameter online identification, model predictive control, and a multi-actuator collaborative framework, utilizing intelligent algorithms for parameter optimization. Theoretical and simulation analyses—based on the IEA 15&#xa0;MW reference wind turbine model (simulated via Bladed + MATLAB/Simulink, with environmental parameters: wind speed 3 ~ 25&#xa0;m/s, wave height 2 ~ 8&#xa0;m, water temperature 10 ~ 30℃)—confirm improved energy conversion efficiency (+ 3.2% vs. +2.1% in latest studies), reduced fatigue loads (−12.5% vs. −9.8% in peers), and enhanced operational stability. It offers a new avenue for offshore wind power intelligent control.</p>

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Adaptive whole-machine loss calculation methodology within sensor-based control framework for large-scale offshore floating wind turbines

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月13日

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  • Yan Li,
  • Zheng Zhang,
  • Peng Hao,
  • Weidong Ji,
  • Rongfu Li,
  • Xiaoyong Li

摘要

As the offshore wind energy sector advances into deeper, remote sea areas with escalating single-turbine power, the capacity of large floating wind turbines has expanded, heightening demands for control system precision. Traditional static loss model-based strategies struggle to meet dynamic operational needs in complex marine settings. This paper introduces a collaborative control method leveraging adaptive modeling of whole-machine dynamic losses. By developing a multi-physics coupling model and integrating real-time condition sensing with adaptive algorithms, it enables dynamic monitoring and active regulation of global losses. This approach combines multi-dimensional parameter online identification, model predictive control, and a multi-actuator collaborative framework, utilizing intelligent algorithms for parameter optimization. Theoretical and simulation analyses—based on the IEA 15 MW reference wind turbine model (simulated via Bladed + MATLAB/Simulink, with environmental parameters: wind speed 3 ~ 25 m/s, wave height 2 ~ 8 m, water temperature 10 ~ 30℃)—confirm improved energy conversion efficiency (+ 3.2% vs. +2.1% in latest studies), reduced fatigue loads (−12.5% vs. −9.8% in peers), and enhanced operational stability. It offers a new avenue for offshore wind power intelligent control.