[1]张建华,张梦佳,黄德豪,等.基于数据驱动的风电场等效建模及主动尾流控制[J].郑州大学学报(工学版),2025,46(06):66-74.[doi:10.13705/j.issn.1671-6833.2025.03.009]
　ZHANG Jianhua,ZHANG Mengjia,HUANG Dehao,et al.Data-driven Equivalent Modeling and Active Wake Control of a Wind Farm[J].Journal of Zhengzhou University (Engineering Science),2025,46(06):66-74.[doi:10.13705/j.issn.1671-6833.2025.03.009]
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基于数据驱动的风电场等效建模及主动尾流控制()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
46
期数:
2025年06期
页码:
66-74
栏目:
出版日期:
2025-10-22

文章信息/Info

Title:
Data-driven Equivalent Modeling and Active Wake Control of a Wind Farm
文章编号:
1671-6833(2025)06-0066-09
作者:
张建华;  张梦佳;  黄德豪;  赵　思
华北电力大学 控制与计算机工程学院,北京 102206
Author(s):
ZHANG Jianhua;  ZHANG Mengjia;  HUANG Dehao;  ZHAO Si
School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China
关键词:
风电场;  尾流效应;  Informer神经网络;  主动尾流控制;  粒子群优化算法
Keywords:
wind farms;  wake effect;  Informer neural network;  active wake control;  particle swarm optimization
分类号:
TM614TM73TP18
DOI:
10.13705/j.issn.1671-6833.2025.03.009
文献标志码:
A
摘要:
为减小尾流干扰对风电场的总输出功率的影响,在提出的风电场偏航优化控制框架中,设计了一种Informer神经网络算法,建立了面向偏航控制风电场输出功率等效模型。在此模型的基础上,进一步提出了以风机偏航角为决策变量的场级输出功率最大化问题,采用粒子群优化算法进行求解,以获得各风力机的最优偏航角,从而有效减小场间尾流干扰。首先,搭建了一个由14台风机组成、布局为Penmanshiel风电场的模拟风电场;其次,利用风力数据对风电场进行等效建模,并将Informer模型结果与LSTM、GRU、RNN、Transformer等模型结果进行对比。结果表明:所建立的Informer智能等效模型能较好地契合风电场的实际特性,将所提算法与螳螂搜索算法进行比较,在风速10 m/s、风向195°的风况下,所提算法使得风电场总功率提升了1.94 MW;在连续风况(某日实测风数据)下,风电场总功率平均提升292.97 kW,提升效果均优于螳螂搜索算法,验证了所提算法能很好地提高风电场整体输出功率。
Abstract:
In order to reduce the influence of wake disturbance on the total output power of wind farm, Informer neural network algorithm was proposed in the proposed wind farm yaw optimization control framework, and an intelligent equivalent model of power conversion for wind farm yaw control was established. Based on the present model, an optimization problem maximizing the power output of wind farm with yaw angles as decision variables was defined, and particle swarm optimization algorithm was used to obtain the optimal yaw angle of each wind turbine and reduce the wake interference. Firstly, a wind farm consisting of 14 wind turbines was built,and its layout was Penmanshiel wind farm. Secondly, wind data was used to model the wind farm equivalently, and the results of the Informer model were compared with LSTM, GRU, RNN, and Transformer. The results showed that the established Informer intelligent equivalent model could consist with the actual characteristics of the wind farms. Comparing the proposed algorithm with the mantis search algorithm, the proposed algorithm could increase the total power of wind farms by 1.94 MW with the wind speed of 10 m/s and the wind direction of 195°. With continuous wind conditions (measured wind data on a certain day), the total power of the wind farm was increased by 292.97 kW on average, and the improvement results were superior to the mantis search algorithm. The proposed algorithm could improve the overall output power of the wind farm well.

参考文献/References:

[1]HOWLAND M F, LELE S K, DABIRI J O. Wind farm power optimization through wake steering[J]. Proceedings of the National Academy of Sciences of the United States of America, 2019, 116(29): 14495-14500. 

[2]BENSASON D, SIMLEY E, ROBERTS O, et al. Evaluation of the potential for wake steering for U. S. landbased wind power plants[J]. Journal of Renewable & Sustainable Energy. 2021, 13(3): 033303. 
[3]宗豪华, 孙恩博. 水平轴风力机主动尾流控制综述[J]. 空气动力学学报, 2022, 40(4): 51-68. 
ZONG H H, SUN E B. Reivew of active wake control for horizontal-axis wind turbines[J]. Acta Aerodynamica Sinica, 2022, 40(4): 51-68. 
[4]邓智文, 郭苏, 许昌, 等. 海上风电场功率提升和疲劳平衡综合优化控制[J]. 太阳能学报, 2021, 42 (1): 180-186. 
DENG Z W, GUO S, XU C, et al. Comprehensive optimization control of power boost and fatigue balance for offshore wind farms[J]. Acta Energiae Solaris Sinica, 2021, 42(1): 180-186. 
[5]胡阳, 张冲, 房方, 等. 基于主动尾流控制的风电机群协同优化调度[J]. 动力工程学报, 2024, 44(4): 566-574. 
HU Y, ZHANG C, FANG F, et al. Cooperative and optimal scheduling of wind turbine groups based on active wake control[J]. Journal of Chinese Society of Power Engineering, 2024, 44(4): 566-574. 
[6]刘一格, 赵振宙, 马远卓, 等. 基于鲸鱼优化算法的串列风力机主动尾流控制策略[J]. 中国电机工程学报, 2024, 44(9): 3702-3710. 
LIU Y G, ZHAO Z Z, MA Y Z, et al. Active wake control strategy of tandem wind turbines based on whale optimization algorithm[J]. Proceedings of the CSEE, 2024, 44(9): 3702-3710. 
[7]ZHANG Z Y, HUANG P, BITSUAMLAK G, et al. Analytical solutions for yawed wind-turbine wakes with application to wind-farm power optimization by active yaw control[J]. Ocean Engineering, 2024, 304: 117691. 
[8]CAI W, HU Y, FANG F, et al. Wind farm power production and fatigue load optimization based on dynamic partitioning and wake redirection of wind turbines[J]. Applied Energy, 2023, 339: 121000. 
[9]GUO N Z, ZHANG M M, LI B. A data-driven analytical model for wind turbine wakes using machine learning method[J]. Energy Conversion and Management, 2022, 252: 115130. 
[10] SUN H Y, QIU C Y, LU L, et al. Wind turbine power modelling and optimization using artificial neural network with wind field experimental data[J]. Applied Energy, 2020, 280: 115880. 
[11]焦小敏, 耿华, 马少康, 等. 基于数据驱动的多风电机组协同控制方法[J]. 电源学报, 2020, 18(2): 2431. 
JIAO X M, GENG H, MA S K, et al. Data-driven cooperative control method for multiple wind turbines[J]. Journal of Power Supply, 2020, 18(2): 24-31. 
[12] RAK B P, SANTOS PEREIRA R B. Impact of the wake deficit model on wind farm yield: a study of yaw-based control optimization[J]. Journal of Wind Engineering and Industrial Aerodynamics, 2022, 220: 104827. 
[13] SONG D R, FAN X Y, YANG J, et al. Power extraction efficiency optimization of horizontal-axis wind turbines through optimizing control parameters of yaw control systems using an intelligent method[J]. Applied Energy, 2018, 224: 267-279. 
[14] PARK J, LAW K H. Bayesian ascent: a data-driven optimization scheme for real-time control with application to wind farm power maximization[J]. IEEE Transactions on Control Systems Technology, 2016, 24(5): 1655-1668. 
[15] VALI M, PETROVIC ′ V, PAO L Y, et al. Model predictive active power control for optimal structural load equalization in waked wind farms[J]. IEEE Transactions on Control Systems Technology, 2022, 30(1): 30-44. 
[16] YIN X X, ZHANG W C, JIANG Z S, et al. Data-drivenmulti-objective predictive control of offshore wind farm based on evolutionary optimization[J]. Renewable Energy, 2020, 160: 974-986. 
[17] HANSEN M O L. Aerodynamics of wind turbines[M]. 3rd ed. London: Routledge, 2015. 
[18] SHAKOOR R, HASSAN M Y, RAHEEM A, et al. Wake effect modeling: a review of wind farm layout optimization using Jensen′s model[J]. Renewable and Sustainable Energy Reviews, 2016, 58: 1048-1059. 
[19] BASTANKHAH M, PORTÉ-AGEL F. Experimental and theoretical study of wind turbine wakes in yawed conditions [J]. Journal of Fluid Mechanics, 2016, 806: 506-541. 
[20] PARK J, LAW K H. A data-driven, cooperative wind farm control to maximize the total power production[J]. Applied Energy, 2016, 165: 151-165. 
[21] ZHOU H Y, ZHANG S H, PENG J Q, et al. Informer: beyond efficient transformer for long sequence time-series forecasting[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(12): 11106-11115. 
[22] NASH R, NOURI R, VASEL-BE-HAGH A. Wind turbine wake control strategies: a review and concept proposal[J]. Energy Conversion and Management, 2021, 245: 114581. 
[23]陈婧华, 张琳娟, 卢丹, 等. 基于改进粒子群优化算法的分布式电源集群划分方法[J]. 郑州大学学报(工学版), 2023, 44(5): 77-85. 
CHEN J H, ZHANG L J, LU D, et al. Cluster partition method of distributed power supply based on improved particle swarm optimization algorithm[J]. Journal of Zhengzhou University (Engineering Science), 2023, 44 (5): 77-85. 
[24] GEBRAAD P M O, TEEUWISSE F W, VAN WINGERDEN J W, et al. Wind plant power optimization through yaw control using a parametric model for wake effects: a CFD simulation study[J]. Wind Energy, 2016, 19(1): 95-114. 
[25] JONKMAN J, BUTTERFIELD S, MUSIAL W, et al. Definition of a 5-MW reference wind turbine for offshore system development[EB/OL]. (2009-02-01)[202407-03]. https:∥digital. library. unt. edu/ark:167531/ metadc894033/ .

更新日期/Last Update: 2025-10-21