基于Koopman算子的风电机组RMPC控制

李士哲; 陈培栋

doi:10.19912/j.0254-0096.tynxb.2024-1922

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太阳能学报 ›› 2026, Vol. 47 ›› Issue (3) : 261-268. DOI: 10.19912/j.0254-0096.tynxb.2024-1922

基于Koopman算子的风电机组RMPC控制

李士哲, 陈培栋

作者信息 +

RMPC-BASED CONTROL OF WIND TURBINES USING KOOPMAN OPERATOR

Li Shizhe, Chen Peidong

Author information +

文章历史 +

摘要

结合Koopman算子和鲁棒的方法,提出基于Koopman算子的风电机组鲁棒模型预测控制（RMPC）。依据动力学原理,构建风电机组及其风速的数学模型,并收集数据建立基于Koopman的线性预测模型。通过Matlab仿真验证模型的有效性。针对预测模型的误差结合鲁棒的方法进行管理。仿真结果表明,改进方法有效提高风电机组的鲁棒性。

Abstract

A robust model predictive control （RMPC） approach for wind turbines is introduced in this paper, which integrates the Koopman operator with robust control strategies. A dynamic model of the wind turbine and its wind speed model is derived based on fundamental principles. By collecting relevant data, a Koopman-based linear prediction model is developed and validated through Matlab simulations. To address model prediction errors, robust techniques are introduced for error management. Simulation results show that the proposed method enhances the overall robustness of the wind turbine system.

导出引用

李士哲, 陈培栋. 基于Koopman算子的风电机组RMPC控制[J]. 太阳能学报. 2026, 47(3): 261-268 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1922

Li Shizhe, Chen Peidong. RMPC-BASED CONTROL OF WIND TURBINES USING KOOPMAN OPERATOR[J]. Acta Energiae Solaris Sinica. 2026, 47(3): 261-268 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1922

中图分类号： TM614

参考文献

[1] SARKAR M R, JULAI S, TONG C W, et al.Hybrid pitch angle controller approaches for stable wind turbine power under variable wind speed[J]. Energies, 2020, 13(14): 3622.
[2] 孙欣宇, 向东, 丁伟, 等. 基于麻雀搜索算法的风电机组独立变桨控制研究[J]. 太阳能学报, 2023, 44(10): 266-274.
SUN X Y, XIANG D, DING W, et al.Research on individual pitch control of wind turbine based on sparrow search algorithm[J]. Acta energiae solaris sinica, 2023, 44(10): 266-274.
[3] QU F, HU H, XU R T, et al.Super-twisted sliding mode control for maximum power point tracking of wind turbine based on neural network[J]. Journal of circuits, systems and computers, 2023, 32(18): 2350315.
[4] ZHANG J H, LIU L Y, LIU Y Q, et al.Research on robust model predictive control strategy of wind turbines to reduce wind power fluctuation[J]. Electric power systems research, 2022, 213: 108809.
[5] 田春筝, 王浚怡, 蒋小亮, 等. 基于模糊控制与风速分区的风电机组一次调频控制策略研究[J]. 可再生能源, 2025, 43(1): 124-133.
TIAN C Z, WANG J Y, JIANG X L, et al.Research on primary frequency modulation control strategy of wind turbines based on fuzzy control and wind speed zoning[J]. Renewable energy resources, 2025, 43(1): 124-133.
[6] 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]. 中国电机工程学报, 2025, 45(11): 4230-4244.
AO B Y, 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, 2025, 45(11): 4230-4244.
[7] GHORBANI SHEKTAEI S R, SADATI N. Multi model robust control design for a floating offshore variable speed wind turbine with tension leg platform[J]. Ocean engineering, 2022, 266: 113033.
[8] SIERRA-GARCIA J E, SANTOS M. Neural networks and reinforcement learning in wind turbine control[J]. Revista iberoamericana de automatica E informatica industrial, 2021, 18(4): 327-335.
[9] 刘军, 全哲辰, 朱世祥, 等. 考虑疲劳载荷的双馈风电机组自适应LQG一次调频控制策略[J]. 电力系统自动化, 2025, 49(9): 175-186.
LIU J, QUAN Z C, ZHU S X, et al.Adaptive linear quadratic Gaussian primary frequency regulation control strategy for doubly-fed wind turbines considering fatigue loads[J]. Automation of electric power systems, 2025, 49(9): 175-186.
[10] ROUTRAY A, HUR S H.Full operational envelope control of a wind turbine using model predictive control[J]. IEEE access, 2022, 10: 121940-121956.
[11] 赵正阳, 杜钦君, 凌辉, 等. 改进的同步磁阻电机模型预测转矩控制[J]. 太阳能学报, 2023, 44(6): 469-476.
ZHAO Z Y, DU Q J, LING H, et al.Improved model predictie torque control of synchronous reluctance motor[J]. Acta energiae solaris sinica, 2023, 44(6): 469-476.
[12] 田德, 周臣凯, 唐世泽, 等. 基于自适应模型预测控制的大型风电机组MPPT方法[J]. 太阳能学报, 2023, 44(6): 501-508.
TIAN D, ZHOU C K, TANG S Z, et al.MPPT method for large wind turbines based on adaptive model predictive control[J]. Acta energiae solaris sinica, 2023, 44(6): 501-508.
[16] LIU X J, FENG L, KONG X B.A comparative study of robust MPC and stochastic MPC of wind power generation system[J]. Energies, 2022, 15(13): 4814.
[13] TANG M N, WANG W J, YAN Y G, et al.Robust model predictive control of wind turbines based on Bayesian parameter self-optimization[J]. Frontiers in energy research, 2023, 11: 1306167.
[13] TANG M N, WANG W J, YAN Y G, et al.Robust model predictive control of wind turbines based on Bayesian parameter self-optimization[J]. Frontiers in energy research, 2023, 11: 1306167.
[14] HUSHAM A, KAMWA I, ABIDO M A, et al.Decentralized stability enhancement of DFIG-based wind farms in large power systems: Koopman theoretic approach[J]. IEEE access, 2022, 10: 27684-27697.
[14] HUSHAM A, KAMWA I, ABIDO M A, et al.Decentralized stability enhancement of DFIG-based wind farms in large power systems: koopman theoretic approach[J]. IEEE access, 2022, 10: 27684-27697.
[15] KORDA M, MEZIĆ I.Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control[J]. Automatica, 2018, 93: 149-160.
[15] KORDA M, MEZIĆ I.Linear predictors for nonlinear dynamical systems: Koopman operator meets model predictive control[J]. Automatica, 2018, 93: 149-160.
[16] LIU X J, FENG L, KONG X B.A comparative study of robust MPC and stochastic MPC of wind power generation system[J]. Energies, 2022, 15(13): 4814.
[17] ZHANG X L, PAN W, SCATTOLINI R, et al.Robust tube-based model predictive control with Koopman operators[J]. Automatica, 2022, 137: 110114.
[18] MAYNE D Q, SERON M M, RAKOVIĆ S V.Robust model predictive control of constrained linear systems with bounded disturbances[J]. Automatica, 2005, 41(2): 219-224.
[19] RAWINGS J B, MAYNE D Q, DIEHL M.Model predictive control: theory, computation, and design[M]. Madison, WI: Nob Hill Publishing, 2017: 193-246.