使用新型卡尔曼滤波对风速、风向测量结果进行数据校正。借助一组样本点发挥非线性函数作用,使风速、风向垂直分布状态的估计结果更准确,经过若干次迭代,逐步降低合成风速、风向的测量误差。实验结果表明,在不同距离范围内,该方法能保证风速、风向测量误差的均值不变且近似于0,误差的标准差降低60%以上。
Abstract
For the vertical distribution of wind speed and direction in front of wind turbines can not be accurately described by linear wind shear model, the measurement of wind filed reconstruction using four-beam lidar is prone to measurement errors. In order to solve this problem and take advantage of the simplicity and low cost of this method, A novel Kalman filtering was proposed to correct the measured data of wind speed and direction measurements. This method estimates the wind speed and direction accurately using a set of sample points to approximate the nonlinear function. After several iteration, this method reduce the measured error of wind speed and direction gradually. Experimental results show that, the novel method ensures the mean of measured error of wend speed and direction is close to zeros, and the standard deviation of the error is reduced by 60%.
关键词
风电机组 /
激光雷达 /
卡尔曼滤波 /
风速 /
风向
Key words
wind turbines /
lidar /
Kalman filtering /
wind speed /
wind direction
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 刘航. 基于激光雷达的偏航控制技术研究[D]. 杭州: 浙江大学, 2019.
LIU H.Research on yaw control technology based on lidar[D]. Hangzhou: Zhejiang University, 2019.
[2] SHI R J, FAN X C, HE Y.Comprehensive evaluation index system for wind power utilization levels in wind farms in China[J]. Renewable and sustainable energy reviews, 2017, 69: 461-471.
[3] YANG J B, LIU Q Y, LI X, et al.Overview of wind power in China: status and future[J]. Sustainability, 2017, 9(8): 1454.
[4] LAKS J, PAO L, SIMLEY E, et al. Model predictive control using preview measurements from LIDAR[C]//49th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition, Orlando, Florida, 2011: AIAA2011-813.
[5] MERCIECA J, ARAM P, JONES B L, et al.A spatiotemporal estimation framework for real-world LIDAR wind speed measurements[J]. IEEE transactions on control systems technology, 2019, 28(4): 1595-1602.
[6] 潘超, 李润宇, 王典, 等. 基于风速时空关联的多步预测方法[J]. 太阳能学报, 2022, 43(2): 458-464.
PAN C, LI R Y, WANG D, et al.Multi-step wind speed prediction method based on wind speed spatial-time correlation[J]. Acta energiae solaris sinica, 2022, 43(2): 458-464.
[7] 罗颖, 刘雨辰, 米立华, 等. 基于WRF模拟和注意力机制的短期风速预测[J]. 太阳能学报, 2023, 44(9): 302-310.
LUO Y, LIU Y C, MI L H, et al.Short-term wind speed forecast based on WRF simulation and attention mechanism[J]. Acta energiae solaris sinica, 2023, 44(9): 302-310.
[8] SOLEIMANZADEH M, WISNIEWSKI R, BRAND A.State-space representation of the wind flow model in wind farms[J]. Wind energy, 2014, 17(4): 627-639.
[9] SEKAR A P K, VAN DOOREN M F, MIKKELSEN T, et al. Evaluation of the LINCOM wind field reconstruction method with simulations and full-scale measurements[J]. Journal of Physics: conference series, 2018, 1037: 052008.
[10] VAN DOOREN M, TRABUCCHI D, KÜHN M. A methodology for the reconstruction of 2D horizontal wind fields of wind turbine wakes based on dual-Doppler lidar measurements[J]. Remote sensing, 2016, 8(10): 809.
[11] STOCK W C, MAZOYER P, COMBREXELLE S.Wind field reconstruction from lidar measurements at high-frequency using machine learning[J]. Journal of physics: conference series, 2018, 1102: 012003.
[12] RAISSI M, PERDIKARIS P, KARNIADAKIS G E.Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations[J]. Journal of computational physics, 2019, 378: 686-707.
[13] 李娜. 基于相似日的PSO-ELM算法的短期负荷预测研究[J]. 太原学院学报(自然科学版), 2020, 38(3): 19-22.
LI N.Short-term load forecasting based on similar days PSO-ELM algorithm[J]. Journal of Taiyuan University (natural science edition), 2020, 38(3): 19-22.
[14] ZHANG J C, ZHAO X W.Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements[J]. Applied energy, 2021, 288: 116641.
[15] 王喜泉, 何山, 王维庆, 等. 基于激光测风雷达及SSA-ELM的风电场短期风速预测[J]. 电网与清洁能源, 2022, 38(5): 120-128.
WANG X Q, HE S, WANG W Q, et al.Short-term wind speed prediction of wind farms based on laser wind measurement radar and SSA-ELM[J]. Power system and clean energy, 2022, 38(5): 120-128.
[16] 陈文婷, 张铂炅, 林勇刚, 等. 基于激光雷达的风力机分离测风独立变桨控制策略[J]. 太阳能学报, 2022, 43(2): 415-423.
CHEN W T, ZHANG B J, LIN Y G, et al.Separated wind measurement individual pitch control of wind turbine based on lidar[J]. Acta energiae solaris sinica, 2022, 43(2): 415-423.
[17] 张鹏, 魏龙超, 沈静, 等. 一种基于卡尔曼滤波的激光雷达合成风速及风向处理方法: 202311849983[P].2024-03-12.
ZHANG P, WEI L C, SHEN J, et al. A method for processing synthetic wind speed and direction of lidar based on kalman filter: 202311849983[P].2024-03-12.
PDF(1772 KB)
