Damage Identification in a Laboratory Offshore Wind Turbine Demonstrator

被引:3
|
作者
Gomez Gonzalez, Ana [1 ]
Zugasti, Ekhi [2 ]
Anduaga, Javier [2 ]
机构
[1] Univ Santiago de Compostela, Dept Appl Math, Santiago De Compostela, Spain
[2] IK 4 Ikerlan, Departent Sensors, Gipuzkoa, Spain
来源
关键词
Damage identification; Structural Health Monitoring; AutoRegressive method; EIGENFREQUENCY CHANGES; FAULT-DETECTION;
D O I
10.4028/www.scientific.net/KEM.569-570.555
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
This paper presents a method to detect and identify damage in a laboratory offshore wind turbine support structure. The structure consists of three different parts: the jacket, the tower and the nacelle. The jacket is a lattice structure joined with several bolts. The tower consists of three different sections joined by bolts. The nacelle is composed of a single piece. The different parts are also joined with bolts. The damage in the structure is simulated by loosening some of the bolts in the jacket. Two damage detection algorithms, namely AutoRegressive methods and NullSpace methods, have been tested in a primitive variation of this structure without the jacket, obtaining good results. In this paper we present the application of the last damage detection method to the new structure with the jacket and an extension to identification of the damage.
引用
收藏
页码:555 / +
页数:3
相关论文
共 50 条
  • [21] Damage identification of a jacket support structure for offshore wind turbines
    University of Agder, Department of Engineering Sciences, Grimstad, Norway
    Proc. IEEE Conf. Ind. Electron. Appl., ICIEA, (995-1000):
  • [22] Influence of Corrosion Damage on Fatigue Limit Capacities of Offshore Wind Turbine Substructure
    Li, Ying
    Zhang, Yu
    Wang, Wenhua
    Li, Xin
    Wang, Bin
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2022, 10 (08)
  • [23] Offshore Wind Turbine Jacket Damage Detection via a Siamese Neural Network
    Tutiven, Christian
    Baquerizo, Joseph
    Vidal, Yolanda
    Puruncajas, Bryan
    Sampietro, Jose
    EUROPEAN WORKSHOP ON STRUCTURAL HEALTH MONITORING (EWSHM 2022), VOL 1, 2023, 253 : 113 - 122
  • [24] Fatigue damage assessment of fixed offshore wind turbine tripod support structures
    Yeter, B.
    Garbatov, Y.
    Guedes Soares, C.
    ENGINEERING STRUCTURES, 2015, 101 : 518 - 528
  • [25] Data fusion-based damage identification for a monopile offshore wind turbine structure using wireless smart sensors
    Jeong, Seunghoo
    Kim, Eun-Jin
    Shin, Do Hyoung
    Park, Jong-Woong
    Sim, Sung-Han
    OCEAN ENGINEERING, 2020, 195
  • [26] Damage identification of an offshore wind turbine foundation based on a CP algorithm combined with the method of DE-GWO-SVR
    Du Z.
    Shao X.
    Wang X.
    Zhendong yu Chongji/Journal of Vibration and Shock, 2020, 39 (22): : 110 - 118
  • [27] Damage Identification of Wind Turbine Blades Using Piezoelectric Transducers
    Choi, Seong-Won
    Farinholt, Kevin M.
    Taylor, Stuart G.
    Light-Marquez, Abraham
    Park, Gyuhae
    SHOCK AND VIBRATION, 2014, 2014
  • [28] Damage identification of wind turbine blades based on acoustic emission
    Wang, Zihan
    Xu, Kaidi
    Zhang, Jia
    Bi, Yanzhao
    Luo, Zhichun
    Insight: Non-Destructive Testing and Condition Monitoring, 2022, 64 (05): : 279 - 284
  • [29] Damage identification of wind turbine blades based on dynamic characteristics
    Su, Tian
    Su, Wei
    Du, Chenyu
    Huang, Zhanfang
    Dong, Jianping
    Hu, Chao
    NONLINEAR ENGINEERING - MODELING AND APPLICATION, 2022, 11 (01): : 47 - 57
  • [30] Wind turbine bearing damage identification based on VEITD and OSMHD
    Tang G.
    Zhu X.
    Wang X.
    Xue G.
    Xu Z.
    Zhou W.
    Dianli Zidonghua Shebei/Electric Power Automation Equipment, 2023, 43 (06): : 101 - 107