在基于叶片根部载荷的PID独立变桨控制的基础上,引入激光雷达并提出优化的独立变桨控制方法。利用激光雷达可重建风力机前方风场信息的特点,对风力机前方风速进行提前测量。提出并使用统一风演化模型对所测数据进行二次处理,得到更贴近实际的叶轮中心风速,进一步使用所提出的分离测风方法对叶根载荷进行提前计算,根据载荷的计算值进行独立变桨控制。该方法可用于解决由于信号延迟、变桨执行机构延迟导致的风速与桨距角不匹配问题。在保证发电功率平稳的情况下,进一步减小风力机工作时的叶根载荷及轮毂不平衡载荷。使用TurbSim软件生成风模型,并采用Matlab/Simulink及FAST进行联合仿真。仿真结果表明,在额定风况下,不平衡载荷相较于传统独立变桨方法有8.07%~11.17%的减小量,阵风风况下可将冲击载荷峰值减小32.06%。
Abstract
Based on the traditional PID controller of individual pitch control（IPC）, an optimized control method is proposed by introducing the lidar. The lidar can be used to reconstruct the characteristics of the wind field information in front of the wind turbine, predict the wind speed and direction in front of the totor in advance, and the measured data can be processed by using the unified wind evolution model to obtain the wind speed in the center of the totor that is closer to the reality. Furthermore, the method of separated wind measurement（SWM） proposed in this paper is used to calculate the blade root load in advance, and the blade root load is controlled by IPC according to the calculated value of load. This method is used to solve the problem of mismatch between wind speed and pitch angle caused by signal delay and rotor actuator delay. The blade root load and hub unbalance load can be further reduced under the condition of ensuring stable power generation. The wind model is generated with TurbSim software and co-simulated with Matlab/Simulink and FAST. The simulation results show that the unbalanced load can be reduced by 8.07% to 11.17% under the rated wind condition, and the peak load can be reduced by 32.06% under the gust wind condition.
关键词
风电机组 /
激光雷达 /
风速演化 /
分离测风 /
独立变桨
Key words
wind turbines /
lidar /
atmospheric movements /
separated wind measurement /
individual pitch control
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参考文献
[1] BOSSANYI E A.Individual blade pitch control for loadreduction[J]. Wind energy, 2003, 6(2): 119-128.
[2] LIO W H, JOES L B, ROSSITER J A.Estimation and control of wind turbine tower vibrations based on Individual Blade-PitchStrategies[J]. IEEE transactions on control systems technology, 2019, 27(4): 1820-1828.
[3] MENEZES E J N, ARAUJO A M, DASILVA N S B. A review on wind turbine control and itsassociatedmethods[J]. Journal of cleaner production, 2018, 174: 945-953.
[4] PETROVIC V, JELAVIC M, BAOTIC M.Advanced control algorithms for reduction of wind turbine structural loads[J]. Renew energy, 2015, 76: 418-431.
[5] HOGHOOGHI H, CHOKANI N, ABHARI R S.Effectiveness of individual pitch control on a 5 MW downwind turbine[J]. Renewable energy, 2019, 139: 435-446.
[6] TIAN W, PAN W X, SHAO Y X, et al.Individual pitch control strategy for reducing aerodynamic loads and torque ripples[J]. IEEJ transactions on electrical and electronic engineering, 2019, 14(11): 1624-1632.
[7] LI B J, LIO W H, ROSSITER J A.Overcoming fundamental limitations of wind turbine individual blade pitch control with inflow sensors[J]. Wind energy, 2018, 21(10): 922-936.
[8] SANTHANAGOPALAN V, ROTEA M A, IUNGO G V.Performance optimization of a wind turbine column for different incoming wind turbulence[J]. Renewable energy, 2018, 116(Part B): 232-243.
[9] AITKEN M L, RHODES M E, LUNDQUIST J K.Performance of a wind-profiling lidar in the region of wind turbine rotor disks[J]. Journal of atmospheric and oceanic technology, 2012, 29(3): 347-355.
[10] ZHAN L, LETIZIA S, IUNGO G V.LiDAR measurements for an onshore wind farm: wake variability for different incoming wind speeds and atmospheric stability regimes[J]. Wind energy, 2019, 23(3): 501-527.
[11] EL-ASHA S, ZHAN L, V IUNGO G. Quantification of power losses due to wind turbine wake interactions through SCADA, meteorological and wind LiDAR data[J]. Wind energy, 2017, 20(11): 1823-1839.
[12] DIMITROV N, BORRACCINO A, PENA A, et al.Windturbine load validation using lidar-based wind retrievals[J]. Wind Energy, 2019, 22(11): 1512-1533.
[13] BORRACCINO A.Remotely measuring the wind using turbine-mountedlidars: application to power performance testing[D]. Technical University of Denmark, Copenhagen, DTU Wind Energy, 2017.
[14] 汪海蛟, 江全元. 应用于平抑风电功率波动的储能系统控制与配置综述[J]. 电力系统自动化, 2014, 38(19): 126-135.
WANG H J, JIANG Q Y.An overview of control and configuration of energy storage system used for wind power fluctuation mitigation[J]. Automation of electric power systems, 2014, 38(19): 126-135.
[15] SØRENSEN P, HANSEN A D, ROSAS P A C. Wind models for simulation of power fluctuations from wind farms[J]. Journal of wind engineering and industrial aerodynamics, 2002, 90(12-15):1381-1402.
[16] 应有, 许国东, 潘东浩, 等. 大型风电机组独立变桨控制技术研究[J]. 太阳能学报, 2011, 32(6): 891-896.
YING Y, XU G D, PAN D H, et al.Individual pitch control for large scale wind turbine[J]. Acta energiae solaris sinica, 2011, 32(6): 891-896.
[17] 马欣欣, 陈进, 李世六. 风力机叶片在三维湍流下的载荷分析[J]. 可再生能源, 2009, 27(5): 19-22.
MA X X, CHEN J, LI S L.Analysis on the wind turbine's blade loads in 3D turbulence[J]. Renewable energy,2009, 27(5): 19-22.
[18] BOSSANYI E A.GH bladed versin 3.67 user manual[M]. England: Garrad Hassan and Partner Limited, 2005.
[19] 秦本双. 基于实测数据的风电场风速和风功率预测研究[D]. 吉林: 东北电力大学, 2018.
QIN B S.Research on the wind speed and wind power forecasting based on actual measured data[D]. Jilin: Northeast Electric Power University, 2018.
[20] 田盛华, 王欣, 秦斌, 等. 激光雷达测风建模与模拟[J]. 湖南工业大学学报, 2019, 33(5): 62-66.
TIAN S H, WAMG X, QIN B, et al.LIDAR wind measurement modeling and simulation[J]. Journal of Hunan University of Technology, 2019, 33(5): 62-66.
[21] 吴杰, 丁明. 采用自适应小波包分解的混合储能平抑风电波控制策略[J]. 电力系统自动化, 2017, 41(3): 7-12.
WU J, DING M.Wind power fluctuation smoothing strategy of hybrid energy storage system using self-adaptive wavelet packet decomposition[J]. Automation of electric power systems, 2017, 41(3): 7-12.
[22] UNGURAN R, BOERSMA S, PETROVIC V, et al.Feedback-feedforward individual pitch control design with uncertain measurements[C]//American Control Conference, Philadelphia, 2019.
[23] MEDICI D, IVANELL S, DAHLBERG J, et al.The upstream flow of a wind turbine: blockage effect[J]. Wind energy, 2011, 14(5): 691-697.
基金
国家自然科学基金创新研究科学群体科学基金（51821093）; 浙江省自然基金重点项目（LZ19E050001）; 浙江省博后择优项目（zj2019020）
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