EN风力机叶片损伤故障检测技术研究进展
Research Progress on Blade Damage Fault Detection Technology of Wind Turbine
- 发电技术 2020年41卷第6期 页码：599-607
- 作者机构：
长沙理工大学能源与动力工程学院, 湖南省 长沙市 410014
- 作者简介：
[ "杨昌杏(1994), 男, 硕士研究生, 研究方向为动力机械状态检测与故障诊断, ycx1451353576@163.com" ]
李录平(1963), 男, 教授, 博士生导师, 研究方向为动力机械状态检测与故障诊断, 本文通信作者, cs_liluping@163.com
- 基金信息：国家自然科学基金项目(51405033)
DOI：10.12096/j.2096-4528.pgt.20044
中图分类号： TK83;TM315收稿：2020-06-20，
纸质出版：2020-12-31
- 稿件说明：
移动端阅览
杨昌杏, 李录平. 风力机叶片损伤故障检测技术研究进展[J]. 发电技术, 2020,41(6):599-607.
Changxing YANG, Luping LI. Research Progress on Blade Damage Fault Detection Technology of Wind Turbine[J]. Power Generation Technology, 2020, 41(6): 599-607.
杨昌杏, 李录平. 风力机叶片损伤故障检测技术研究进展[J]. 发电技术, 2020,41(6):599-607. DOI： 10.12096/j.2096-4528.pgt.20044.
Changxing YANG, Luping LI. Research Progress on Blade Damage Fault Detection Technology of Wind Turbine[J]. Power Generation Technology, 2020, 41(6): 599-607. DOI： 10.12096/j.2096-4528.pgt.20044.
摘要
针对目前风力机叶片损伤状态检测和故障诊断的方法开展综述，指出其研究现状和值得研究的问题。综述了风力机叶片的故障类型及其对应的故障机理以及5种常用的风力机叶片状态检测和故障诊断的方法。通过分析各种检测技术的优缺点发现，各种损伤检测技术都能够对风力机叶片存在的缺陷进行有效的检测，但是单一的检测技术已经无法满足对叶片损伤故障检测的要求，因此，可以采用多种检测技术相结合的方式对叶片进行综合检测。
Abstract
The methods of damage state detection and fault diagnosis of wind turbine blades were reviewed
and the research status and problems worthy of study were pointed out. The fault types and corresponding fault mechanism of wind turbine blades
several commonly used methods of state detection and fault diagnosis of wind turbine blades were summarized. The advantages and disadvantages of various detection techniques were analyzed. It is found that all kinds of damage detection technology can effectively detect defects in wind turbine blades
but single detection technology has been unable to meet the requirements of blade damage fault detection. Therefore
a variety of detection techniques can be used to conduct comprehensive detection of the blade.
关键词
Keywords
references
许国东, 叶杭冶, 解鸿斌.风电机组技术现状及发展方向[J].中国工程科学, 2018, 20(3):44-50.
XU G D, YE H Y, XIE H B.Current situation and development direction of wind turbine technology[J].China Engineering Science, 2008, 20(3):44-50.
刘永前, 赖福兴, 阎洁, 等.考虑不同截面翼型选取的风电机组叶片优化设计方法[J].发电技术, 2019, 40(4):382-388.
LIU Y Q, LAI F X, YAN J, et al.Optimal design method for wind turbine blades considering aerofoil of cross-section[J].Power Generation Technology, 2019, 40(4):382-388.
王琳琳, 陈长征, 周勃, 等.风力机叶片复合材料裂尖温度场及微观损伤研究[J].发电技术, 2019, 40(6):605-610.
WANG L L, CHEN C Z, ZHOU B, et al.Study on crack tip temperature field and microstructure damage for wind turbine composite blade[J].Power Generation Technology, 2019, 40(6):605-610.
李刚, 胡红利, 李亚妮.风力发电机叶片状态监测与故障诊断技术近况[J].工业仪表与自动化装置, 2017(5):16-20.
LI G, HU H L, LI Y N.Recent status of wind turbine blade status monitoring and fault diagnosis technology[J].Industrial Instrumentation and Automation Devices, 2017(5):16-20.
仇梓峰, 王爽心, 李蒙.基于无人机图像的风力发电机叶片缺陷识别[J].发电技术, 2018, 39(3):277-285.
QIU Z F, WANG S X, LI M.Defect detection of wind turbine blade based on unmanned aerial vehicle-taken Images[J].Power Generation Technology, 2018, 39(3):277-285.
赵勇, 韩斌, 房刚利.风力发电机状态监测与故障诊断技术综述[J].热力发电, 2016, 45(10):1-5.
ZHAO Y, HAN B, FANG G L.Overview of wind turbine state monitoring and fault diagnosis technology[J].Thermal Power Generation, 2016, 45(10):1-5.
CHEN J B, SONG Y P, PENG Y B, et al.An efficient rotational sampling method of wind fields for wind turbine blade fatigue analysis[J].Renewable Energy, 2020, 146(2):2170-2187.
李芒芒.风力机叶片机械损伤动态监测方法与技术研究[D].长沙: 长沙理工大学, 2012.
LI M M.Research on dynamic monitoring methods and technologies for mechanical damage of wind turbine Blades[D].Changsha: Changsha University of Science and Technology, 2012.
张磊, 马云玲, 赵贺, 等.风力机叶片故障诊断方法研究[J].企业导报, 2015(19):164.
ZHANG L, MA Y L, ZHAO H, et al.Study on fault diagnosis methods of wind turbine Blades[J].Enterprise Guide, 2015(19):164.
MOHAMMAD M R, MEHDI B, HAMED M, et al.Modal-based damage identification for the nonlinear model of modern wind turbine blade[J].Renewable Energy, 2016, 94(8):391-409.
周勃, 谷艳玲, 项宏伟, 等.风力机叶片裂纹扩展预测与疲劳损伤评价[J].太阳能学报, 2015, 36(1):41-48.
ZHOU B, GU Y L, XIANG H W, et al.Prediction of crack growth and fatigue damage evaluation of wind turbine blade[J].Acta Solar Energy, 2015, 36(1):41-48.
龚妙, 李录平, 刘瑞, 等.基于运行参数特征的风力机叶片覆冰诊断方法[J].动力工程学报, 2019, 39(3):214-219.
GONG M, LI L P, LIU R, et al.Wind turbine blade icing diagnosis based on operating parameter characteristics[J].Chinese Journal of Power Engineering, 2019, 39(3):214-219.
石凯凯, 蔡力勋, 包陈.预测疲劳裂纹扩展的多种理论模型研究[J].机械工程学报, 2014, 50(18):50-58.
SHI K K, CAI L X, BAO C.Multiple theoretical models for predicting fatigue crack growth[J].Chinese Journal of Mechanical Engineering, 2014, 50(18):50-58.
周勃, 陈维涛, 贺森亮, 等.风力发电机叶片多裂纹随机扩展和损伤容限研究[J].太阳能学报, 2015, 36(12):2837-2843.
ZHOU B, CHEN W T, HE S L, et al.Study on random crack growth and damage tolerance of wind turbine blades[J].Acta Solar Energy, 2015, 36(12):2837-2843.
ZHANG M M, TAN B, XU J Z.Smart fatigue load control on the large-scale wind turbine blades using different sensing signals[J].Renewable Energy, 2016, 87(1):111-119.
李丹, 邱颖宁, 冯延晖.风力发电系统警报信号故障诊断[J].太阳能学报, 2017, 38(11):3138-3143.
LI D, QIU Y N, FENG Y H.Fault diagnosis of wind power system warning signal[J].Journal of Solar Energy, 2017, 38(11):3138-3143.
陈镇南, 卢绪祥, 李录平, 等.基于ANSYS/LS-DYNA的带冠叶片碰撞振动特性的有限元分析[J].动力工程学报, 2013, 33(3):189-193.
CHEN Z N, LU X X, LI L P, et al.Finite element analysis based on the collision vibration characteristics of crowned blades of ANSYS/LS-DYNA[J].Dynamic Engineering, 2013, 33(3):189-193.
李海波, 卢绪祥, 李录平, 等.风力机叶片动力特性实验台设计[J].热能动力工程, 2012, 27(1):117-121.
LI H B, LU X X, LI L P, et al.Wind turbine blade power characteristics test bench design[J].Thermal Power Engineering, 2012, 27(1):117-121.
陈雪峰, 李继猛, 程航, 等.风力发电机状态监测和故障诊断技术的研究与进展[J].机械工程学报, 2011, 47(9):45-52.
CHEN X F, LI J M, CHENG H, et al.Research and progress in wind turbine condition monitoring and troubleshooting technology[J].Mechanical Engineering Journal, 2011, 47(9):45-52.
LEE J K, PARK J Y, OH K Y, et al.Transformation algorithm of wind turbine blade moment signals for blade condition monitoring[J].Renewable Energy, 2014.
李录平, 封江, 杨波.风力机叶片寿命损耗计算方法与工程应用[J].长沙理工大学学报(自然科学版), 2017, 14(4):83-89.
LI L P, FENG J, YANG B.Wind turbine blade life loss calculation method and engineering application[J].Changsha University of Technology Journal (Natural Science), 2017, 14(4):83-89.
陈彩凤, 成斌, 李西洋, 等.风力发电机叶片模态分析及参数优化[J].水力发电, 2018, 44(9):85-88.
CHEN C F, CHENG B, LI X Y, et al.Wind turbine blade modal analysis and parameter optimization[J].Hydropower, 2018, 44(9):85-88.
陈顺章, 李录平, 晋风华, 等.风力机桨叶的三维建模与动力学特性有限元计算[J].可再生能源, 2010, 28(3):29-34.
CHEN S Z, LI L P, JIN F H, et al.3D modeling of wind turbine blades and the finite element of dynamics[J].Renewable Energy, 2010, 28(3):29-34.
黄敏, 徐菲, 刘珺.基于云计算与物联网的风力发电智能监测系统研究[J].可再生能源, 2017, 35(7):1032-1037.
HUANG M, XU F, LIU W.Research on intelligent monitoring systems for wind power based on cloud computing and the Internet of Things[J].Renewable Energy, 2017, 35(7):1032-1037.
赵勇, 王冰佳, 王杰斌.常用风力机叶片无损检测技术对比分析[J].热力发电, 2015, 44(12):7-10.
ZHAO Y, WANG B J, WANG J B.Comparative analysis of commonly used wind turbine blade[J].Thermal Power Generation, 2015, 44(12):7-10.
王志刚, 杨波, 陈志刚, 等.运行状态下的风力机叶片在线监测技术进展[J].热能动力工程, 2017, 32(S1):1-5.
WANG Z G, YANG B, CHEN Z G, et al.Advances in the online monitoring technology of wind turbine blades in operational state[J].Thermal Power Engineering, 2017, 32(S1):1-5.
刘胜先, 李录平, 余涛, 等.基于振动检测的风力机叶片覆冰状态诊断技术[J].中国电机工程学报, 2013, 33(32):88-95.
LIU S X, LI L P, YU T, et al.Seismic-based wind turbine blade ice-covered status diagnostic technology[J].China Electrical Engineering Journal, 2013, 33(32):88-95.
雷利斌, 李录平, 刘胜先, 等.基于振型曲率的风力机叶片覆冰检测技术[J].太阳能学报, 2014, 35(5):841-847.
LEI L B, LI L P, LIU S X, et al.Wind turbine blade ice-covering detection technology based on vibration-type curvature[J].Solar Journal, 2014, 35(5):841-847.
李录平, 李芒芒, 晋风华, 等.振动检测技术在风力机叶片裂纹故障监测中的应用[J].热能动力工程, 2013, 28(2):207-212.
LI L P, LI M M, JIN F H, et al.Application of vibration detection technology in the monitoring of crack faults in wind turbine blades[J].Thermal Power Engineering, 2013, 28(2):207-212.
欧阳涛.基于叶尖定时的旋转叶片振动检测及参数辨识技术[D].天津: 天津大学, 2011.
OUYANG T.Rotating blade vibration detection and parameter identification technology based on leaf tip timing[D].Tianjin: Tianjin University, 2011.
陈广华, 田德, 李琪.风力发电机组叶片振动无线检测系统[J].电子技术应用, 2012, 38(5):80-83.
CHEN G H, TiAN D, LI Q.Wind turbine blade vibration wireless detection system[J].Electronic Technology Applications, 2012, 38(5):80-83.
LIU Z P, ZHANG L, CARRASCO J.Vibration analysis for large-scale wind turbine blade bearing fault detection with an empirical wavelet thresholding method[J].Renewable Energy, 2020, 146:99-110.
陈志刚, 李录平, 杨波, 等.风电机组叶片覆冰状态与动力学特性的定量关系研究[J].风能, 2018(1):60-65.
CHEN Z G, LI L P, YANG B, et al.Quantitative relationship between wind turbine blade ice cover and dynamics study[J].Wind Energy, 2018(1):60-65.
谭海辉, 李录平, 朱益军, 等.覆冰对风力机叶片动力特性影响的有限元分析[J].可再生能源, 2010, 28(4):33-38.
TAN H H, LI L P, ZHU Y J, et al.A finite element analysis of the impact of ice cover on the power characteristics of wind turbine blades[J].Renewable Energy, 2010, 28(4):33-38.
李录平, 刘胜先, 谭海辉, 等.气温和空气湿度对桨叶覆冰特性影响的实验研究[J].热能动力工程, 2012, 27(5):610-614.
LI L P, LIU S X, TAN H H, et al.Experimental study of the effects of temperature and air humidity on the characteristics of blade ice cover[J].Thermal Power Engineering, 2012, 27(5):610-614.
李录平, 颜健, 封江, 等.基于WiFi技术的风力机叶片覆冰监测系统研究[J].可再生能源, 2016, 34(4):543-549.
LI L P, YAN J, FENG J, et al.Research on the wind turbine blade ice-covering monitoring system based on WiFi technology[J].Renewable energy, 2016, 34(4):543-549.
颜健, 李录平, 雷利斌, 等.风力机桨叶超声波除冰实验技术研究及其应用[J].可再生能源, 2015, 33(1):68-74.
YAN J, LI L P, LEI L B, et al.Wind turbine blade ultrasonic de-icing experimental technology research and its application[J].Renewable Energy, 2015, 33(1):68-74.
谭海辉, 李录平, 靳攀科, 等.风力机叶片超声波除冰理论与方法[J].中国电机工程学报, 2010, 30(35):112-117.
TAN H H, LI L P, JIN P K, et al.Wind turbine blade ultrasonic de-icing theory and methods[J].China Electric Engineering Journal, 2010, 30(35):112-117.
李录平, 谭海辉, 卢绪祥, 等.层状结构中的超声传播理论及其在风力机桨叶除冰中的应用[J].中国电机工程学报, 2012, 32(17):125-132.
LI L P, TAN H H, LU X X, et al.The theory of ultrasonic propagation in the layered structure and its application in de-icing the blades of wind turbines[J].China Electrical Engineering Journal, 2012, 32(17):125-132.
TIWARI K A, RAISUTIS R.Post processing of ultrasonic signals for the analysis of defects in wind turbine blade using guided waves[J].The Journal of Strain Analysis for Engineering Design, 2018, 53(12):128-139.
王绍龙.基于超声波法的风力机叶片翼型防除冰研究[D].哈尔滨: 东北农业大学, 2014.
WANG S L.Research on anti-deicing of wind turbine Blade Airfoil Based on ultrasonic Wave method[D].Harbin: Northeast Agricultural University, 2014.
琦格琦.基于红外热成像技术的风力机叶片涂层损伤监测的研究[D].呼和浩特: 内蒙古农业大学, 2019.
QI G Q.Study on the damage monitoring of wind turbine blade coating based on infrared thermal imaging technology[D].Hohhot: Inner Mongolia Agricultural University, 2019.
肖劲松, 严天鹏.风力机叶片的红外热成像无损检测的数值研究[J].北京工业大学学报, 2006, 32(1):48-52.
XIAO J S, YAN T P.Numerical study on nondestructive testing of wind turbine Blades by infrared Thermal Imaging[J]. Journal of Beijing University of Technology, 2006, 32(1):48-52.
MITJA M, LOVRENC N, MIHAEL S.Measurements on rotating blades using IR thermography[J].Experimental Thermal and Fluid Science, 2007, 32(2):387-396.
JORDAN N, KIRAN B, RAJITHA M, et al.Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction[J].Energy, 2020, 190(1):153-162.
田磊.信息化控制技术在风力发电中的应用[J].信息记录材料, 2019, 20(8):151-152.
TIAN L.Application of information control technology in wind power generation[J].Information Recording Materials, 2019, 20(8):151-152.
龚妙, 李录平.声发射技术在风力机叶片故障检测中的应用研究综述[J].太阳能, 2018(5):57-61.
GONG M, LI L P.Review of the application of acoustic emission technology in the detection of wind turbine blade fault[J].Solar Energy, 2018(5):57-61.
周勃, 张士伟, 陈长征, 等.风力机叶片多裂纹扩展声发射信号的特征识别[J].仪器仪表学报, 2015, 36(1):110-117.
ZHOU B, ZHANG S W, CHEN C Z, et al.Feature recognition of the multi-crack expansion sound emission signal of wind turbine blades[J].Instrumentation Journal, 2015, 36(1):110-117.
TANG J L, SOUA S, MARES C, et al.An experimental study of acoustic emission methodology for in service condition monitoring of wind turbine blades[J].Renewable Energy, 2016, 99(12):170-179.
杜文超.基于声发射技术的大型风力机叶片材料的损伤研究[D].南京: 南京航空航天大学, 2012.
DU W C.Damage study on blade materials of large-scale wind turbine based on acoustic emission technology[D].Nanjing: Nanjing University of Aeronautics and Astronautics, 2012.
陈娟子.基于光纤光栅的风机叶片应变监测关键技术研究[D].成都: 西南交通大学, 2012.
CHEN J Z.Research on key technology of FBG based fan blade strain monitoring[D].Chengdu: Southwest Jiaotong University, 2012.
李俊一, 薛鹏, 朱晓非, 等.光纤光栅技术在风力发电机组叶片检测中的应用[J].风能产业, 2017(6):78-82.
LI J Y, XUE P, ZHU X F, et al.The application of fiber-optic grating technology in blade detection in wind turbines[J].Wind Energy Industry, 2017(6):78-82.
TYLER J A, AJIT A, PIER M, et al.Development of a FBG based distributed strain sensor system for wind turbine structural health monitoring[J].Smart Materials and Structures, 2013, 22(7):126-135.
李继超.基于FBG风电桨叶动平衡监测系统的研究[J].企业技术开发, 2015, 34(6):1-2.
LI J C.Research on FBG wind power blade dynamic balance monitoring system[J].Enterprise technology development, 2015, 34(6):1-2.
0
浏览量
0
下载量
0
CSCD
- 网络PDF下载
- Meta-XML下载
- BibTeX下载
- Endnote下载
- RefMan 下载
- NoteExpress下载
- NoteFirst下载
关联资源
相关文章
相关作者
相关机构
AI问答- 技术支持由北京北大方正电子有限公司提供 京ICP备09064830号-19
京公网安备11010802024621 - 本系统建议在Chrome、 IE9+ 以上版本浏览器阅读本站内容，360浏览器请切换至极速模式
- Cookies帮助我们提供服务并提供个性化体验。使用本网站，即表示您同意我们使用Cookies