基于本征正交分解的风电机组结构在线状态监测

金晓航, 杨昊旋, 郭远晶, 张杰, 彭光健, 翁泽宇

太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 547-555.

PDF(3457 KB)
欢迎访问《太阳能学报》官方网站,今天是
PDF(3457 KB)
太阳能学报 ›› 2025, Vol. 46 ›› Issue (5) : 547-555. DOI: 10.19912/j.0254-0096.tynxb.2024-0119

基于本征正交分解的风电机组结构在线状态监测

  • 金晓航1~3, 杨昊旋2, 郭远晶4, 张杰1, 彭光健2,3, 翁泽宇2,3
作者信息 +

CONDITION MONITORING OF WIND TURBINE STRUCTURE COMPONENTS BASED ON PROPER ORTHOGONAL DECOMPOSITION

  • Jin Xiaohang1~3, Yang Haoxuan2, Guo Yuanjing4, Zhang Jie1, Peng Guangjian2,3, Weng Zeyu2,3
Author information +
文章历史 +

摘要

为掌握风电机组结构健康状态信息,降低运维成本,避免倒塔、叶片断裂等重大故障的发生,提出一种基于本征正交分解的风电机组大型结构部件的状态监测方法。所提方法使用本征正交分解对结构有限元模型进行降维分析,可减少计算成本,达到在线监测的目的。针对塔筒与叶片的实际情况,分别构建有限元模型并降阶:将塔筒简化为梁的静力学问题,使用本征正交分解构建降阶模型,搭建试验平台验证方法的可行性;将叶片简化为中心刚体-柔性梁模型,通过模型的降阶分析,快速计算获取叶片的瞬态响应。通过与其他方法的对比分析以及结果的讨论,表明所提方法可有效实现风电机组结构部件健康状态的在线监测。

Abstract

In order to monitor the health status of wind turbine structure, reduce operation and maintenance costs, and avoid failures such as tower collapse and blade breakage, a condition monitoring approach for wind turbines is proposed. The proposed approach employs proper orthogonal decomposition (POD) to perform dimensionality reduction analysis on the finite element model. It reduces the computational costs to achieve the online monitoring goal. Finite element models are constructed and analyzed based on the actual conditions of the tower and blades, respectively. The tower is simplified as a beam for static analysis. uses POD is used to create a reduced-order model, and an experimental platform is established to validate the reduced-order model. The blade is simplified as a central rigid-flexible beam model. The transient response of the blade can be analyzed using the reduced order model. Comparative analysis and discussion of the results indicate that the proposed approach enables online monitoring of the health status of wind turbine structural components.

关键词

本征正交分解 / 有限元法 / 风电机组 / 状态监测 / 塔筒 / 叶片

Key words

proper orthogonal decomposition / finite element method / wind turbines / condition monitoring / tower / blades

引用本文

导出引用
金晓航, 杨昊旋, 郭远晶, 张杰, 彭光健, 翁泽宇. 基于本征正交分解的风电机组结构在线状态监测[J]. 太阳能学报. 2025, 46(5): 547-555 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0119
Jin Xiaohang, Yang Haoxuan, Guo Yuanjing, Zhang Jie, Peng Guangjian, Weng Zeyu. CONDITION MONITORING OF WIND TURBINE STRUCTURE COMPONENTS BASED ON PROPER ORTHOGONAL DECOMPOSITION[J]. Acta Energiae Solaris Sinica. 2025, 46(5): 547-555 https://doi.org/10.19912/j.0254-0096.tynxb.2024-0119
中图分类号: TH17   

参考文献

[1] 金晓航, 泮恒拓, 徐正国. 数据驱动的风电机组变桨系统状态监测[J]. 太阳能学报, 2022, 43(4): 409-417.
JIN X H, PAN H T, XU Z G.Condition monitoring of wind turbine pitch system using data-driven approach[J]. Acta energiae solaris sinica, 2022, 43(4): 409-417.
[2] 龙寰, 杨婷, 徐劭辉, 等. 基于数据驱动的风电机组状态监测与故障诊断技术综述[J]. 电力系统自动化, 2023, 47(23): 55-69.
LONG H, YANG T, XU S H, et al.Overview of data-driven state monitoring and fault diagnosis technology for wind turbines[J]. Automation of electric power systems, 2023, 47(23): 55-69.
[3] 李东东, 华伟, 郑小霞, 等. 基于似然学习机的风电机组齿轮箱状态监测技术研究[J]. 太阳能学报, 2021, 42(4): 374-379.
LI D D, HUA W, ZHENG X X, et al.Study on wind turbine gearbox status monitoring based on likelihood learning machine[J]. Acta energiae solaris sinica, 2021, 42(4): 374-379.
[4] JIN X H, XU Z W, QIAO W.Condition monitoring of wind turbine generators using SCADA data analysis[J]. IEEE transactions on sustainable energy, 2021, 12(1): 202-210.
[5] 尹诗, 侯国莲, 于晓东, 等. 基于SCADA数据的风电机组齿轮箱状态监测方法[J]. 太阳能学报, 2021, 42(1): 324-332.
YIN S, HOU G L, YU X D, et al.Condition monitoring method of wind turbine gear box based on SCADA data[J]. Acta energiae solaris sinica, 2021, 42(1): 324-332.
[6] 郭鹏, 徐明, 白楠, 等. 基于SCADA运行数据的风电机组塔架振动建模与监测[J]. 中国电机工程学报, 2013, 33(5): 128-135.
GUO P, XU M, BAI N, et al.Wind turbine tower vibration modeling and monitoring driven by SCADA data[J]. Proceedings of the CSEE, 2013, 33(5): 128-135.
[7] 金晓航, 泮恒拓, 许壮伟, 等. 基于SCADA数据和单分类简化核极限学习机的风电机组发电机状态监测[J]. 计算机集成制造系统, 2022, 28(8): 2408-2418.
JIN X H, PAN H T, XU Z W, et al.Condition monitoring of wind turbine generators using SCADA data and OC-RKELM[J]. Computer integrated manufacturing systems, 2022, 28(8): 2408-2418.
[8] 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18.
TAO F, LIU W R, LIU J H, et al.Digital twin and its potential application exploration[J]. Computer integrated manufacturing systems, 2018, 24(1): 1-18.
[9] 刘大同, 郭凯, 王本宽, 等. 数字孪生技术综述与展望[J]. 仪器仪表学报, 2018, 39(11): 1-10.
LIU D T, GUO K, WANG B K, et al.Summary and perspective survey on digital twin technology[J]. Chinese journal of scientific instrument, 2018, 39(11): 1-10.
[10] 李录贤, 刘书静, 张慧华, 等. 广义有限元方法研究进展[J]. 应用力学学报, 2009, 26(1): 96-108.
LI L X, LIU S J, ZHANG H H, et al.Researching progress of generalized finite element method[J]. Chinese journal of applied mechanics, 2009, 26(1): 96-108.
[11] 董雷霆, 周轩, 赵福斌, 等. 飞机结构数字孪生关键建模仿真技术[J]. 航空学报, 2021, 42(3): 107-135.
DONG L T, ZHOU X, ZHAO F B, et al.Key technologies for modeling and simulation of airframe digital twin[J]. Acta aeronautica et astronautica sinica, 2021, 42(3): 107-135.
[12] 赵延玉, 赵晓永, 王磊, 等. 可解释人工智能研究综述[J]. 计算机工程与应用, 2023, 59(14): 1-14.
ZHAO Y Y, ZHAO X Y, WANG L, et al.Review of explainable artificial intelligence[J]. Computer engineering and applications, 2023, 59(14): 1-14.
[13] 路宽, 张亦弛, 靳玉林, 等. 本征正交分解在数据处理中的应用及展望[J]. 动力学与控制学报, 2022, 20(5): 20-33.
LU K, ZHANG Y C, JIN Y L, et al.Application and outlook of proper orthogonal decomposition in data processing[J]. Journal of dynamics and control, 2022, 20(5): 20-33.
[14] 蒋耀林. 模型降阶方法[M]. 北京: 科学出版社, 2010.
JIANG Y L.Model order reduction methods[M]. Beijing: Science Press, 2010.
[15] 张正, 刘杰. 一种基于Galerkin映射减基法的结构参数反求方法[J]. 中国机械工程, 2014, 25(14): 1951-1955.
ZHANG Z, LIU J.An identifying structural parameter technique based on Galerkin mapping-reduced basis method[J]. China mechanical engineering, 2014, 25(14): 1951-1955.
[16] 肖青, 周少武. 基于阿基米德Copula和拉丁超立方采样的概率最优潮流计算[J]. 电力自动化设备, 2019, 39(11): 174-180.
XIAO Q, ZHOU S W.Probabilistic optimal power flow computation based on Archimedean Copula and Latin hypercube sampling[J]. Electric power automation equipment, 2019, 39(11): 174-180.
[17] 蔡国平, 洪嘉振. 考虑附加质量的中心刚体-柔性悬臂梁系统的动力特性研究[J]. 机械工程学报, 2005, 41(2): 33-40.
CAI G P, HONG J Z.Dynamics study of hub-beam system with tip mass[J]. Journal of mechanical engineering, 2005, 41(2): 33-40.
[18] 刘齐茂, 燕柳斌. 基于Newmark法敏度计算的刚架结构动力优化[J]. 工程力学, 2010, 27(3): 145-154.
LIU Q M, YAN L B.Dynamic optimization of frame structures using sensitivity calculation based on Newmark method[J]. Engineering mechanics, 2010, 27(3): 145-154.
[19] JONKMAN J, BUTTERFIELD S, MUSIAL W, et al.Definition of a 5-MW reference wind turbine for offshore system development[R]. NREL/TP-500-38060, 2009.
[20] 贺德馨. 风工程与工业空气动力学[M]. 北京: 国防工业出版社, 2006.
HE D X.Wind engineering and industrial aerodynamics[M]. Beijing: National Defense Industry Press, 2006.

基金

国家重点研发计划(2022YFE0198900); 石油天然气装备教育部重点实验室(西南石油大学)资助项目(OGE202302-10)

PDF(3457 KB)

Accesses

Citation

Detail

段落导航
相关文章

/