Structural health monitoring of towers and blades for floating offshore wind turbines using operational modal analysis and modal properties with numerical-sensor signals

OCEAN ENGINEERING

卷:188

Article

Kim, Hyoung-Chul¹  Kim, Moo-Hyun¹  Choe, Do-Eun² 

[1] Texas A&M Univ, Ocean Engn Dept, College Stn, TX 77843 USA

[2] Prairie View A&M Univ, Civil & Environm Engn Dept, Prairie View, TX 77446 USA

关键词: Operational modal analysis;    Floating offshore wind turbine;    Health monitoring;    Damage detection;    Curvature mode shape;    Numerical sensor;    Turbine-floater-mooring coupled dynamics simulation;   

DOI  :  10.1016/j.oceaneng.2019.106226

来源: Elsevier

【 摘 要 】

In the present study, a structural health monitoring (SHM) method for floating offshore wind turbines (FOWTs) is suggested and tested using operational modal analysis (OMA) with numerical-sensor signals. The numerical accelerometer signals along the tower and blade of FOWT in dynamic wind field are used for the OMA. The numerical-sensor signals are simulated using a time-domain turbine-floater-mooring fully-coupled dynamic simulation computer program. To perform the SHM of a FOWT through OMA, natural frequencies, displacement mode shapes (DMS), and curvature mode shapes (CMS) of the tower and blades are obtained and analyzed. The modal properties are systematically compared between the intact and damaged conditions. Their differences are used for damage detection. The results show that CMS is found to be the most effective modal property to detect damage locations and intensities. The performance of the damage detection based on the OMA-CMS analysis is verified by independent FEM (finite element method) results. This study is unique in that the SHM using OMA and CMS for FOWTs is implemented including the floater-tower-blade coupling effects. The technology can contribute to the design of remote SHM system for future FOWTs. A further validated model with field data may build up a huge database for various damage scenarios so that it can be applied to digital-twin-based smart health monitoring technology.

【 授权许可】

Free