Multi-source Data-driven Yaw Steady-state Error Calibration Method for Wind Turbines and Field Application Testing

QU Chenzhi; LIN Zhongwei; LIU Jizhen; XIE Zhen; CHEN Pei; CHEN Zhenyu; CHEN Ling

doi:10.19805/j.cnki.jcspe.2025.240025

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Multi-source Data-driven Yaw Steady-state Error Calibration Method for Wind Turbines and Field Application Testing

更新时间：2026-01-27

- Multi-source Data-driven Yaw Steady-state Error Calibration Method for Wind Turbines and Field Application Testing
- Chinese Journal of Power Engineering Vol. 45, Issue 4, Pages: 544-553(2025)
- 作者机构：
  
  1. 华北电力大学控制与计算机工程学院,北京,102206
  2. 华北电力大学新能源电力系统全国重点实验室,北京,102206
  3. 中国华能集团有限公司,北京,100031
  4. 中国电力工程顾问集团中南电力设计院有限公司,湖北,武汉,430071
- 作者简介：
- 基金信息：
- DOI：10.19805/j.cnki.jcspe.2025.240025
  CLC： TM315
- Published Online：28 April 2025，
  
  Published：2025
- 稿件说明：
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曲晨志,林忠伟,刘吉臻,谢镇,陈佩,陈振宇,陈玲. 多源数据驱动的风电机组偏航稳态误差校准方法及现场应用测试动力工程学报, 2025, 45(4): 544-553 https://doi.

org/10.19805/j.cnki.jcspe.2025.240025
曲晨志,林忠伟,刘吉臻,谢镇,陈佩,陈振宇,陈玲. 多源数据驱动的风电机组偏航稳态误差校准方法及现场应用测试动力工程学报, 2025, 45(4): 544-553 https://doi. DOI： 10.19805/j.cnki.jcspe.2025.240025.

org/10.19805/j.cnki.jcspe.2025.240025 DOI：

摘要

提出了一种数据驱动的偏航稳态误差校准方法

在不经过大规模硬件改装的条件下

达到改善发电效率的目的。以2 MW水平轴风电机组为研究对象

建立了基于数据采集与监视控制系统(SCADA)数据驱动的稳态误差计算方法

给出量化的误差校准参考;在目标机组上安装了机舱式激光雷达

通过对比多源风向数据

建立了雷达参与稳态误差校准的策略。在综合考虑SCADA和雷达2种数据源计算的误差校准参考情况下

对商用机组进行了工程现场应用及性能评估。结果表明:校准后机组的偏航控制精度和发电效率显著改善

所提方法可用于风电机组增功提效的技术改造。

Abstract

A data-driven yaw steady-state error calibration method was proposed

aiming to improve power generation efficiency without extensive hardware modifications. Taking a 2 MW horizontal-axis wind turbine as the research object

a steady-state error calculation method based on data acquisition and monitoring control system (SCADA) was established

providing a quantitative reference for error calibration. A nacelle-mounted lidar was installed on the target turbine to participate in calibration verification. By analyzing and comparing multi-source wind direction data

a strategy for lidar participation in steady-state error calibration was developed. Considering the error calibration references calculated from both SCADA and lidar data sources

the method was applied and evaluated in the field on a commercial turbine. Results show that the yaw control accuracy and power generation efficiency of the turbine have been improved significantly after calibration. The proposed method can be used for technical upgrades to enhance the performance of wind turbines.

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references

陈以明, 李治. 智慧能源发展方向及趋势分析[J]. 动力工程学报, 2020, 40(10): 852-858, 864. CHEN Yiming, LI Zhi. Analysis on the development trend and features of smart energy sources[J]. Journal of Chinese Society of Power Engineering, 2020, 40(10): 852-858, 864.

陈思, 郭鹏. 基于综合经济效益目标的风电机组偏航控制策略研究[J]. 动力工程学报, 2019, 39(4): 286-292. CHEN Si, GUO Peng. Study on yaw control strategy for wind turbines based on the target of comprehensive economic efficiency[J]. Journal of Chinese Society of Power Engineering, 2019, 39(4): 286-292.

刘吉臻. 支撑新型电力系统建设的电力智能化发展路径[J]. 能源科技, 2022, 20(4): 3-7. LIU Jizhen. Development path of power intelligence supporting the construction of new power system[J]. Energy Science and Technology, 2022, 20(4): 3-7.

梅勇, 李霄, 胡在春, 等. 基于风电机组控制原理的风功率数据识别与清洗方法[J]. 动力工程学报, 2021, 41(4): 316-322, 329. MEI Yong, LI Xiao, HU Zaichun, et al. Identification and cleaning of wind power data methods based on control principle of wind turbine generator system[J]. Journal of Chinese Society of Power Engineering, 2021, 41(4): 316-322, 329.

叶昭良, 王晓东, 尹佐明, 等. 偏航过程中风轮非定常尾流特性研究[J]. 可再生能源, 2019, 37(3): 445-450. YE Zhaoliang, WANG Xiaodong, YIN Zuoming, et al. Investigations on the unsteady wake characteristics of a wind turbine under yawing process[J]. Renewable Energy Resources, 2019, 37(3): 445-450.

QU Chenzhi, LIN Zhongwei, CHEN Pei, et al. An improved data-driven methodology and field-test verification of yaw misalignment calibration on wind turbines[J]. Energy Conversion and Management, 2022, 266: 115786.

YANG Jian, FANG Lingqi, SONG Dongran, et al. Review of control strategy of large horizontal-axis wind turbines yaw system[J]. Wind Energy, 2021, 24(2): 97-115.

MITTELMEIER N, KVHN M. Determination of optimal wind turbine alignment into the wind and detection of alignment changes with SCADA data[J]. Wind Energy Science, 2018, 3(1): 395-408.

PEI Yan, QIAN Zheng, JING Bo, et al. Data-driven method for wind turbine yaw angle sensor zero-point shifting fault detection[J]. Energies, 2018, 11(3): 553.

SONG Dongran, LI Ziqun, WANG Lei, et al. Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation[J]. Applied Energy, 2022, 312: 118773.

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. WANG Xin, WU Genyong, PAN Donghao, et al. Wind turbine yaw control optimization utilizing the running data[J]. Renewable Energy Resources, 2016, 34(3): 413-420.

王欣, 吴根勇, 潘东浩, 等. 基于历史运行数据的风电机组风向标测量误差校准方法[J]. 太阳能学报, 2020, 41(3): 52-58. WANG Xin, WU Genyong, PAN Donghao, et al. Calibration method of wind vane measurement error of wind turbine based on historical operating data[J]. Acta Energiae Solaris Sinica, 2020, 41(3): 52-58.

高鑫. 风电机组振动数据分析与偏航校正[J]. 热能动力工程, 2019, 34(6): 172-177. GAO Xin. Wind turbine vibration data analysis and yaw correction[J]. Journal of Engineering for Thermal Energy and Power, 2019, 34(6): 172-177.

COOPS N C, TOMPALSKI P, GOODBODY T R H, et al. Modelling lidar-derived estimates of forest attributes over space and time: a review of approaches and future trends[J]. Remote Sensing of Environment, 2021, 260: 112477.

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.

DEMURTAS G, PEDERSEN T F, ZAHLE F. Calibration of a spinner anemometer for wind speed measurements[J]. Wind Energy, 2016, 19(11): 2003-2021.

FLEMING P A, SCHOLBROCK A K, JEHU A, et al. Field-test results using a nacelle-mounted lidar for improving wind turbine power capture by reducing yaw misalignment[J]. Journal of Physics: Conference Series, 2014, 524(1): 012002.

International Electrotechnical Commission. Wind energy generation systems-part 12-1: power performance measurements of electricity producing wind turbines:IEC 61400-12-1[S]. UK: IEC, 2017.

刘琳, 郭鹏. 基于改进决策树的多变量功率曲线建模方法[J]. 动力工程学报, 2019, 39(8): 647-653. LIU Lin, GUO Peng. Wind turbine power curve modelling based on improved decision tree considering multiple input variables[J]. Journal of Chinese Society of Power Engineering, 2019, 39(8): 647-653.

BAKHSHI R, SANDBORN P. Maximizing the returns of LIDAR systems in wind farms for yaw error correction applications[J]. Wind Energy, 2020, 23(6): 1408-1421.

中华人民共和国国家质量监督检验检疫总局. 风电场风能资源评估方法: GB/T 18710—2002[S]. 北京: 中国标准出版社, 2004.

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