风电机组风向仪测量误差分析与修正方法

王晓宇,丁同光,摆念宗,高鑫,许炳坤,封新建

分布式能源 ›› 2019, Vol. 4 ›› Issue (6) : 57-62.

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PDF(8205 KB)
分布式能源 ›› 2019, Vol. 4 ›› Issue (6) : 57-62. DOI: 10.16513/j.2096-2185.DE.191082
应用技术

风电机组风向仪测量误差分析与修正方法

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Error Analysis and Correction Method of Wind Direction Meter for Wind Turbine

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文章历史 +

摘要

为提高风电机组在实际运行过程中的偏航精度,采用水平式激光雷达试验与数值模拟相结合的方法对机械式风向仪测量风向的准确性进行研究。通过分析试验数据与模拟结果可得如下结论:风电机叶轮转速小于额定转速,随来流风速的增加,风向仪测量的风向偏差逐渐减小,采用来流风速与偏航角度之间的二次函数对偏航偏差进行修正效果较好;转轮转速达到额定转速,随着来流风速的增加风向仪测量风向偏差维持在常数3.54°附近保持不变,采用常数3.54°对偏航误差进行修正的效果较好;采用分段函数对偏航误差修正后,降低了机组载荷,可提升发电量约1%左右。

Abstract

In order to improve the yaw accuracy of wind turbines in actual operation, the accuracy of wind direction measurement by mechanical anemometer is studied by combining horizontal lidar test with numerical simulation of computational fluid dynamics (CFD). Through analyzing the test data and simulation results, the following conclusions are drawn: 1) When the speed of wind turbine impeller is less than rated speed, and with the increase of the incoming wind speed, the deviation of wind direction measured by the anemometer decreases gradually, and the quadratic function between the incoming wind speed and yaw angle can be used to correct the yaw deviation better. 2) When the rotating speed of the runner reaches the rated speed, and with the increase of the incoming wind speed, the deviation of wind direction measured by the anemometer remains near the constant of 3.54°. When the yaw error is corrected by a constant of 3.54°. 3) The load of the unit is reduced and the generating capacity can be increased by about 1% after the yaw error is corrected by a piecewise function.

关键词

风电机组 / 风向仪 / 偏航 / 数值模拟

Key words

wind turbine / wind direction indicator / yaw / numerical simulation

引用本文

导出引用
王晓宇, 丁同光, 摆念宗, . 风电机组风向仪测量误差分析与修正方法[J]. 分布式能源. 2019, 4(6): 57-62 https://doi.org/10.16513/j.2096-2185.DE.191082
Xiaoyu WANG, Tongguang DING, Nianzong BAI, et al. Error Analysis and Correction Method of Wind Direction Meter for Wind Turbine[J]. Distributed Energy Resources. 2019, 4(6): 57-62 https://doi.org/10.16513/j.2096-2185.DE.191082
中图分类号: TK83   

参考文献

[1]
JIANG Quanyue. Exploration of market operation after wind power generation in China[J]. Economic and Trade Practice, 2018(18): 204-205.
姜全越. 我国风电后市场运营探究[J]. 经贸实践2018(18): 204-205.
[2]
LI Xiaodan. Study on optimal operation of doubly fed wind turbines[D]. Zhengzhou: North China University of Water Resources and Hydropower, 2014.
李晓丹. 双馈风电机组的优化运行研究[D]. 郑州:华北水利水电大学,2014.
[3]
WANG Xin, WU Genyong, PAN Donghao, et al. Research on yaw optimization control method of wind turbine based on operation data[J]. Renewable Energy, 2016, 34 (3): 413-420.
王欣,吴根勇,潘东浩,等. 基于运行数据的风电机组偏航优化控制方法研究[J]. 可再生能源2016, 34(3): 413-420.
[4]
WANG Xiaoyu, ZHAO Xiaqing, XU Bingkun, et al. Research on correction of wind speed and incoming wind speed measured by wind turbine anemometer[J]. Distributed Energy, 2019, 4(3): 63-68.
王晓宇,赵夏青,许炳坤,等. 风力机组风速仪测量风速与来流风速校正研究[J]. 分布式能源2019, 4(3): 63-68.
[5]
DING Xiang, LAN Zhijie, LUO Huabing, et al. Optimum design and application of automatic yaw system for megawatt wind turbine[J]. Electrical Application, 2015, 34(19): 94-97.
丁祥,兰志杰,罗华兵,等. 兆瓦级风电机组自动偏航系统优化设计与应用[J]. 电气应用2015, 34(19): 94-97.
[6]
LI Dazhong, DUAN Liming, YANG Guang. An improved optimizing strategy for maximum power point tracking for wind turbines[J]. Guangdong Electric Power, 2017, 30(8): 59-62.
李大中,段立溟,杨光. 一种改进的大型风电机组最大功率点跟踪优化策略[J]. 广东电力2017, 30(8): 59-62.
[7]
European Norm. Dansk standard wind turbine power performance verification in complex terrain and wind farms: RisØ-R-1330[S]. Denmark, Roskilde: RisØ National laboratory, 2002.
[8]
LAN Wei, ZHANG Zhongquan, LEI Yang, et al. Study on output performance analysis of wind turbine[J]. Power Generation & Air Condition, 2017, 38(3): 26-29, 46.
兰维,张中泉,雷阳,等. 风电机组出力性能分析方法研究[J]. 发电与空调2017, 38(3): 26-29, 46.
[9]
SHEN Xiaojun, ZHOU Chongcheng, FU Xuejiao, et al. Current situation and prospect of wind parameter sensing methods for wind farms[J]. Journal of Tongji University (Natural Science Edition), 2018, 46(9): 1289-1297.
沈小军,周冲成,付雪姣,等. 风电场风参数感知方法现状与展望[J]. 同济大学学报(自然科学版), 2018, 46(9): 1289-1297.
[10]
WAGNER R, COURTNEY M S, PEDERSEN T F, et al. Uncertainty of power curve measurement with a two-beam nacelle-mounted lidar[J]. Wind Energy, 2016, 19(7): 1269-1287.
[11]
张磊,鲁志平,辛克锋,等. 基于分位数的风力发电机组功率曲线绘制方法:CN106089599A[P]. 2016-11-09.
[12]
SITU You, LI Gangqiang, ZHANG Shuiping. Short-term wind speed forecasting based on wavelet decomposition and deep belief network[J]. Guangdong Electric Power, 2017, 30(4): 25-30, 60.
司徒友,李刚强,张水平. 基于小波分解和深度信念网络的短期风速预测[J]. 广东电力2017, 30(4): 25-30, 60.
[13]
WANG Fan. Aerodynamic design and performance analysis of wind turbine blades[D]. Qinhuangdao: Yanshan University, 2016.
王帆. 风力机叶片气动外形设计及性能分析[D]. 秦皇岛:燕山大学,2016.
[14]
WANG Xiaoyu, ZHAO Xiaqing, XU Bingkun, et al. Accuracy of wind turbine anemometer wind measurement based on Simon-wong rigid body algorithm[J]. Energy-saving Technology, 2019, 37(3): 248-254.
王晓宇,赵夏青,许炳坤,等. 基于Simon-wong刚体算法的风力机风速仪测风准确性研究[J]. 节能技术2019, 37(3): 248-254.

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