面向模型预测控制的风电机组深度Koopman全局线性建模方法

田润泽; 寇鹏; 武义琨; 张智豪; 张远航; 王若涛; 郝守礼; 梁得亮

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面向模型预测控制的风电机组深度Koopman全局线性建模方法

更新时间：2025-08-01

- 面向模型预测控制的风电机组深度Koopman全局线性建模方法
- Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbine
- 西安交通大学学报 2025年页码：1-19
- 作者机构：
  
  1.西安交通大学电气工程学院，710049，西安
  2.西安交通大学陕西省智能电网重点实验室，710049，西安
  3.西安交通大学电工材料电气绝缘全国重点实验室，710049，西安
  4.北京京能国际控股有限公司北方分公司，010000，呼和浩特
- 作者简介：
  
  田润泽（1998-），男，博士生
  寇鹏（通信作者），男，教授，博士生导师。
- 基金信息：
  
  国家自然科学基金资助项目(52077165)
- DOI：
  中图分类号： TM614
- 收稿：2025，
  
  网络首发：2025-08-01，
- 稿件说明：
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田润泽,寇鹏,武义琨等.面向模型预测控制的风电机组深度Koopman全局线性建模方法[J].西安交通大学学报,

TIAN Runze,KOU Peng,WU Yikun,et al.Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbine[J].JOURNAL OF XI’AN JIAOTONG UNIVERSITY,
田润泽,寇鹏,武义琨等.面向模型预测控制的风电机组深度Koopman全局线性建模方法[J].西安交通大学学报, DOI：.

TIAN Runze,KOU Peng,WU Yikun,et al.Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbine[J].JOURNAL OF XI’AN JIAOTONG UNIVERSITY, DOI：.

摘要

针对风电机组复杂的非线性动态特性阻碍了面向模型预测控制的机组建模，以及现有非线性和局部线性建模方法在模型复杂度和精度方面存在局限性等问题，提出了一种风电机组全局线性建模方法。首先，基于Koopman算子理论和深度学习技术，设计了状态空间映射神经网络；随后，建立了该网络的训练策略，其特点在于包含一个基于Frobenius范数的正则化项，用以提升所得模型的长期预测精度；最后，通过数据驱动的方式训练所提网络，从而建立起风电机组的高维全局线性动态模型。仿真验证结果表明：所提方法下，风轮转速和桨距角的预测误差分别为0.869%和0.026%，远低于三种对比方法；与局部线性动态模型相比，基于所建高维全局线性动态模型的风电场MPC策略使机组风轮转速跟踪误差和越限程度分别降低了92.58%和95.85%。研究结果可为面向模型预测控制的风电机组动态建模提供理论参考。

Abstract

To address the challenges posed by the complex nonlinear dynamics of wind turbines

which hinder the model predictive control-oriented wind turbine modeling

as well as the limitations of existing nonlinear and local linear modeling methods in model complexity and accuracy

a global linear wind turbine modeling method is proposed. First

based on the Koopman operator theory and deep learning techniques

a state-space mapping neural network is designed. Subsequently

a training strategy is developed for this neural network. The key feature of this strategy is that a Frobenius norm-based regularization term is incorporated to enhance the long-term accuracy of the established model. Finally

by training the proposed neural network in a data-driven manner

the high-dimensional global linear wind turbine dynamic model is established. Simulation results demonstrate that

with the proposed method

the prediction errors of rotor speed and pitch angle are

respectively

0.869% and 0.026%

which is significantly lower than the three comparative methods. Furthermore

compared to the local linear model

the scheme based on the established high-dimensional global linear model reduces the rotor speed tracking error and overshoot by 92.58% and 95.85%

respectively. This study provides a theoretical reference for model predictive control-oriented wind turbine modeling.

关键词

Keywords

references

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