基于间隙度量和模糊控制的漂浮式风电机组变权重多模型预测控制研究

doi:doi:10.19912/j.0254-0096.tynxb.2024-0901

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基于间隙度量和模糊控制的漂浮式风电机组变权重多模型预测控制研究

更新时间：2025-10-22

- 基于间隙度量和模糊控制的漂浮式风电机组变权重多模型预测控制研究
- Vol. 46, Issue 9, Pages: 189-197(2025)
- 作者机构：
- 作者简介：
- 基金信息：
- DOI：doi:10.19912/j.0254-0096.tynxb.2024-0901
  CLC： TM614
- Published Online：14 October 2025，
  
  Published：2025
- 稿件说明：
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[J]. 2025, 46(9): 189-197.
DOI：

[J]. 2025, 46(9): 189-197. DOI： doi:10.19912/j.0254-0096.tynxb.2024-0901.

摘要

该文以NREL 5 MW半潜漂浮式风电机组为研究对象

通过机理和动力学分析构建非线性模型

采用在线参数辨识的方法建立不同工况下的线性变参数（LPV）模型

采用间隙度量理论简化线性模型数量

并与FAST模型进行对比

验证其准确性。提出一种基于模糊变权重的多模型预测（Fuzzy-MPC）变桨控制器

引入卡尔曼滤波状态观测器实现状态量的估计

以减少功率波动和塔筒位移为目标函数

为提高控制器对不同工况的跟踪能力

采用模糊控制在线优化MPC目标函数权重系数

以实现其自适应控制。通过与FAST和定权重MPC控制器对比验证所设计控制器在平缓功率、减少塔筒位移和稳定机组姿态上的有效性。

Abstract

This paper takes the NREL 5MW semi-submersible floating wind turbine as the research object. The nonlinear model was built through mechanisms analysiss and dynamic analysis. The linear parameter-varying （LPV） model under different working conditions was established by the online parameter identification method. The gap metric theory was used to simplify the number of linear models

and the LPV model was compared with the FAST model to verify its accuracy. A multi-model predictive control pitch controller based on fuzzy variable weighting （Fuzzy-MPC） was proposed. The Kalman filter state observer was introduced for state estimation

with to reducing power fluctuations and tower displacement as the objective function. To enhance the controller's tracking ability under different working conditions

the fuzzy control was employed to optimize the weight coefficients of the MPC objective function online

achieving adaptive control. The effectiveness of the designed controller in smoothing power

reducing tower displacement and stabilizing unit attitude is verified by comparison with FAST and fixed weight MPC controllers.

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references

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