STUDY ON ON-LINE LOAD IDENTIFICATION FOR TOWER OF WIND TURBINE BASED ON SURROGATE MODEL

Mou Zheyue; Wang Ruiliang; Zhang Peicheng; Chen Qian; Fan Zenghui

doi:10.19912/j.0254-0096.tynxb.2024-1247

PDF(21365 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 685-691. DOI: 10.19912/j.0254-0096.tynxb.2024-1247

STUDY ON ON-LINE LOAD IDENTIFICATION FOR TOWER OF WIND TURBINE BASED ON SURROGATE MODEL

Mou Zheyue^1,2, Wang Ruiliang^1,2, Zhang Peicheng^1,2, Chen Qian^1,2, Fan Zenghui^1,2

Author information +

History +

Abstract

The tower load source is derived based on the load tranfer mechanicsm of wind turbine, This paper derives the sour tower load based on the load transfer mechanism of wind turbine, and the calculation formulas for tower and nacelle loads are validated using Bladed software. It is found that the rotor loads and nacelle acceleration are the main components of the tower tilt and roll moments. The high correlation between the tower moment loads and rotor loads characteristics including nacelle displacement or tilt angle and generator power, is evaluated with Person coefficient. With the tower bottom load as research subject, the surrogate model for tower bottom load with easy-measuring parameters as input is constructed based on the multiple linear regression algorithm, and the tower bottom resultant moment load can be identified in real time. The effectiveness of model is tested by the simulated loads of large-scale wind turbine, and it is showed that the relative error of ultimate load and equivalent fatigue load between identified and simulated results is controlled within 6%. On this basis, an on-line system for tower load identification and management is proposed.

Key words

wind turbines / towers / dynamic loads / on-line load identification / linear regression / surrogate model

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Mou Zheyue, Wang Ruiliang, Zhang Peicheng, Chen Qian, Fan Zenghui. STUDY ON ON-LINE LOAD IDENTIFICATION FOR TOWER OF WIND TURBINE BASED ON SURROGATE MODEL[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 685-691 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1247

References

[1] CSA C61400-1,Windturbines—part 1: design requirements[S]. CSA, 2014.
[2] GB/T 37257—2018, 风力发电机组机械载荷测量[S].
GB/T 37257—2018, Wind turbines—measurement of mechanical loads[S].
[3] 王瑞良, 孙勇, 李涛, 等. 基于载荷测试的风电机组仿真模型优化与验证[J]. 太阳能学报, 2023, 44(4): 92-98.
WANG R L, SUN Y, LI T, et al.Optimization and verification of wind turbine simulation model based on load test[J]. Acta energiae solaris sinica, 2023, 44(4): 92-98.
[4] 李海波, 张锐, 宫伟, 等. 海上风电机组载荷测试及仿真一致性验证[J]. 船舶工程, 2022, 44(增刊1): 201-207.
LI H B, ZHANG R, GONG W, et al.Load test and simulation consistency verification of offshore wind turbine[J]. Ship engineering, 2022, 44(S1): 201-207.
[5] EVANS M, TAO H, ZHAO S C.Development and validation of real time load estimator on Goldwind 6 MW wind turbine[J]. Journal of physics: conference series, 2018, 1037: 032021.
[6] 薛磊. 大型风电机组载荷监测与延寿评估技术研究[D]. 太原: 山西大学, 2021.
XUE L.Research on load monitoring and life time extension assessment technology of large wind turbine[D]. Taiyuan: Shanxi University, 2020.
[7] 巫发明, 杨从新, 王青, 等. 大型风电机组塔筒动态载荷识别研究与验证[J]. 振动与冲击, 2020, 39(20): 197-203.
WU F M, YANG C X, WANG Q, et al.Research and verification of dynamic load identification for a large wind turbine tower[J]. Journal of vibration and shock, 2020, 39(20): 197-203.
[8] 巫发明. 大型双馈风电机组关键部件动力学特性分析与载荷识别研究[D]. 兰州: 兰州理工大学, 2021.
WU F M.Dynamic characteristics analysis and load identification and verification of key components of large wind turbine[D]. Lanzhou: Lanzhou University of Technology, 2021.
[9] 邓敏强, 邓艾东, 朱静, 等. 基于模态叠加法的风电机组塔架实时状态研究[J]. 太阳能学报, 2021, 42(3): 63-70.
DENG M Q, DENG A D, ZHU J, et al.Research on real-time state of wind turbine tower based on modal superposition method[J]. Acta energiae solaris sinica, 2021, 42(3): 63-70.
[10] BILBAO J, LOURENS E M, SCHULZE A, et al.Virtual sensing in an onshore wind turbine tower using a Gaussian process latent force model[J]. Data-centric engineering, 2022, 3: e35.
[11] BRANLARD E, GIARDINA D, BROWN C S D. Augmented kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations[J]. Wind energy science, 2020, 5(3): 1155-1167.
[12] BRANLARD E, JONKMAN J, DANA S, et al.A digital twin based on OpenFAST linearizations for real-time load and fatigue estimation of land-based turbines[J]. Journal of physics: conference series, 2020, 1618(2): 022030.
[13] WEI D, LI D S, JIANG T, et al.Load identification of a 2.5 MW wind turbine tower using Kalman filtering techniques and BDS data[J]. Engineering structures, 2023, 281: 115763.
[14] 李洁明, 祁新娥. 统计学原理[M]. 5版. 上海: 复旦大学出版社, 2010: 314-317.
LI J M, QI X E.General theory of statistics[M]. 5th ed. Shanghai: Fudan University Press, 2010: 314-317.
[15] 周志华. 机器学习[M]. 北京: 清华大学出版社, 2016.
ZHOU Z H.Machine learning[M]. Beijing: Tsinghua University Press, 2016.