Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbine

TIAN Runze; KOU Peng; WU Yikun; ZHANG Zhihao; ZHANG Yuanhang; WANG Ruotao; HAO Shouli; LIANG Deliang

doi:null

您当前的位置：

首页 >

文章列表页 >

Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbine

更新时间：2025-08-01

- Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbine
- JOURNAL OF XI’AN JIAOTONG UNIVERSITY Pages: 1-19(2025)
- 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：
- 基金信息：
- DOI：
  CLC： TM614
- Received：2025，
  
  Online First：01 August 2025，
- 稿件说明：
移动端阅览
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

李特 , 王荣喜 , 高建民 . 风电机组数据采集与监控系统异常数据识别方法 [J ] . 西安交通大学学报 , 2024 , 58 ( 03 ): 106 - 116 .

LI T , Wang R , Gao J . A method for abnormal data recognition of wind turbine supervisory control and data acquisition systems [J ] . Journal of Xi’an Jiaotong University , 2024 , 58 ( 03 ): 106 - 116 .

鲁宗相 , 李佳明 , 乔颖 , 等 . 新能源场站快速频率支撑能力评估研究现状与技术展望 [J ] . 电力系统自动化 , 2024 , 48 ( 10 ): 1 - 19 .

LU Z , LI J , QIAO Y , et al . Research status and technology prospects of fast frequency support capability assessment for renewable energy stations [J ] . Automation of Electric Power systems , 2024 , 48 ( 10 ): 1 - 19 .

叶林 , 路朋 , 赵永宁 , 等 . 含风电电力系统有功功率模型预测控制方法综述 [J ] . 中国电机工程学报 , 2021 , 41 ( 18 ): 6181 - 6198 .

YE Lin , LU Peng , ZHAO Yongning , et al . Review of model predictive control for power system with large-scale wind power grid-conneced [J ] . Proceedings of the CSEE , 2021 , 41 ( 18 ): 6181 - 6198 .

KOU P , LIANG D , YU L , et al . Nonlinear model predictive control of wind farm for system frequency support [J ] . IEEE Transactions on Power Systems , 2019 , 34 ( 5 ): 3547 - 3561 .

ARAGHI A R , RIAHY G H , CARLSON O , et al . Enhancing the net energy of wind turbine using wind prediction and economic NMPC with high-accuracy nonlinear WT models [J ] . Renewable Energy , 2020 , 151 : 750 - 763 .

Soleymani M , Bigdeli N , Rahmani M . Real-time time-varying economic nonlinear model predictive control for wind turbines [J ] . International Journal of Electrical Power & Energy Systems , 2024 , 159 : 110019 .

Yao Q , Zhang Y , Sun S , et al . Optimization of energy efficiency for offshore wind farms via wake modeling-free NMPC [J ] . Ocean Engineering , 2024 , 305 : 117923 .

LUNA J , FALKENBERG O , GROS S , et al . Wind turbine fatigue reduction based on economic-tracking NMPC with direct ANN fatigue estimation [J ] . Renewable Energy , 2020 , 147 : 1632 - 1641 .

YAO Q , MA B , ZHAO T , et al . Optimized active power dispatching of wind farms considering data-driven fatigue load suppression [J ] . IEEE Transactions on Sustainable Energy , 2022 , 14 ( 1 ): 371 - 380 .

ZHAO H , WU Q , WANG J , et al . Combined active and reactive power control of wind farms based on model predictive control [J ] . IEEE Transactions on Energy Conversion , 2017 , 32 ( 3 ): 1177 - 1187 .

HUANG S , WU Q , BAO W , et al . Hierarchical optimal control for synthetic inertial response of wind farm based on alternating direction method of multipliers [J ] . IEEE Transactions on Sustainable Energy , 2020 , 12 ( 1 ): 25 - 35 .

Cirio D , Conte F , Gabriele B , et al . Fast frequency regulation from a wind farm-BESS unit by model predictive control: Method and hardware-in-the-loop validation [J ] . IEEE Transactions on Sustainable Energy , 2023 , 14 ( 4 ): 2049 - 2061 .

WANG H , YANG Z , CHEN Z , et al . Multiple adaptive model predictive controllers for frequency regulation in wind farms [J ] . IEEE Transactions on Energy Conversion , 2022 , 38 ( 1 ): 15 - 26 .

Ebadollahi S , Saki S . Wind turbine torque oscillation reduction using soft switching multiple model predictive control based on the gap metric and Kalman filter estimator [J ] . IEEE Transactions on Industrial Electronics , 2017 , 65 ( 5 ): 3890 - 3898 .

KOOPMAN B O . Hamiltonian systems and transformation in Hilbert space [J ] . Proceedings of the National Academy of Sciences , 1931 , 17 ( 5 ): 315 - 318 .

GUO Z , WU W . Data-driven model predictive control method for wind farms to provide frequency support [J ] . IEEE Transactions on Energy Conversion , 2021 , 37 ( 2 ): 1304 - 1313 .

HUANG Z , WU W , GUO Z . Data-driven stochastic model predictive control for regulating wind farm power generation with controlled battery storage [J ] . IET Renewable Power Generation , 2023 , 17 ( 14 ): 3568 - 3581 .

孙正龙 , 李浩博 , 刘铖 , 等 . 含虚拟惯量的双馈风电机组扭振阻尼特性分析与抑制方法研究 [J ] . 电网技术 , 2021 , 45 ( 12 ): 4671 - 4683 .

SUN Zhenglong , LI Haobo , LIU Cheng , et al . Torsional oscillation damping characteristics and suppression methods of doubly-fed in-duction generator with virtual inertia [J ] . Power System Technology , 2021 , 45 ( 12 ): 4671 - 4683 .

奥博宇 , 陈磊 , 闵勇 , 等 . 提升变桨减载风电机组调频性能的模糊前馈控制 [J/OL ] . 中国电机工程学报 , 1 - 16 [20250512 ] . http://kns.cnki.net/kcms/detail/11.2107.tm.20250122.1723.002.html http://kns.cnki.net/kcms/detail/11.2107.tm.20250122.1723.002.html .

AO B , CHEN L , MIN Y , et al . Fuzzy feedforward control to improve frequency regulation performance of deloaded wind turbine generator with pitch angle reserve [J ] . Proceedings of the CSEE , 1 - 16 [20250512 ] . http://kns.cn-ki.net/kcms/detail/11.2107.tm.20250122.1723.002.html http://kns.cn-ki.net/kcms/detail/11.2107.tm.20250122.1723.002.html .

KORDA M , MEZIĆ I . Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control [J ] . Automatica , 2018 , 93 : 149 - 160 .

MAMAKOUKAS G , ABRAHAM I , MURPHEY T D . Learning stable models for prediction and control [J ] . IEEE Transactions on Robotics , 2023 , 39 ( 3 ): 2255 - 2275 .

KINGMA D P , BA J . Adam: a method for stochastic optimization [J/OL ] . arxiv : 1412.6980 , 2014 .

GRUNNET J D , SOLTANI M , KNUDSEN T , et al . Aeolus toolbox for dynamics wind farm model, simulation and control [C ] // The European Wind Energy Conference & Exhibition , April 20-23 , 2010 , Warszawa, Poland .

JONKMAN J , BUTTERFIELD S , MUSIAL W , et al . Definition of a 5-MW reference wind turbine for offshore system development [R ] . Golden : National Renewable Energy Laboratory , 2009 .

HUANG S , WU Q , GUO Y , et al . Bi-level decentralised active power control for large-scale wind farm cluster [J ] . IET Renewable Power Generation , 2018 , 12 ( 13 ): 1486 - 1492 .

殷豪 , 曾云 , 孟安波 , 等 . 基于奇异谱分析-模糊信息粒化和极限学习机的风速多步区间预测 [J ] . 电网技术 , 2018 , 42 ( 05 ): 1467 - 1474 .

YIN Hao , ZENG Yun , MENG Anbo , et al . Wind speed multi-step interval prediction based on singular spectrum analysis-fuzzy information granulation and extreme learning machine [J ] . Power System Technology , 2018 , 42 ( 05 ): 1467 - 1474 .

常雨芳 , 杨子潇 , 潘风 , 等 . 基于CEEMDAN-PE-WPD和多目标优化的超短期风电功率预测方法 [J ] . 电网技术 , 2023 , 47 ( 12 ): 5015 - 5026 .

CHANG Yufang , YANG Zixiao , PAN Feng , et al . Ultra-short-term wind power prediction based on CEEMDAN-PE-WPD and multi-objective optimization [J ] . Power System Technology , 2023 , 47 ( 12 ): 5015 - 502

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Model Predictive Control-Oriented Deep Koopman Global Linear Modeling Method for Wind Turbines

Intelligent Quantitative Non-Destructive Evaluation Method for Delamination of Carbon Fiber Composite Tube with Mirror-Reflection Infrared Thermography

Dynamic Model Predictive Control of Grid-Forming Converters

Dynamic Liquid Level Prediction of Steam Generator under Main Steam Pipe Rupture Accidents

A Pure Convolutional Model Fused with U-net Network for Video Prediction

Related Author

TIAN Runze

KOU Peng

WU Yikun

ZHANG Zhihao

ZHANG Yuanhang

WANG Ruotao

HAO Shouli

LIANG Deliang

Related Institution

Northern Branch, Beijing Jingneng International Holding Co., Ltd.

State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University

Shaanxi Key Laboratory of Smart Grid, Xi'an Jiaotong University

School of Electrical Engineering, Xi'an Jiaotong University

School of Aerospace Engineering，Xi’an Jiaotong University

⁰