Vibration source identification of offshore wind turbine structure based on optimized spectral kurtosis and ensemble empirical mode decomposition

Offshore wind turbines are influenced not only by environmental loads such as the wind and waves, but also by various harmonic excitations and have always been considered as complex coupled vibration systems under operational conditions. Generally, the proposed excitations and corresponding vibration sources have a direct relationship with the structural operational safety of the offshore wind turbine and can be reflected by the vibration responses. Nevertheless, existing studies in this area mainly focus on understanding the effect of environmental excitations on the structural vibrations of offshore wind turbines and identifying the dynamic parameters. The research on the identification of the vibration sources and the corresponding energy distribution of offshore wind turbine structures based on the observation data have not yet been carried out. Therefore, using the measured vibration responses of an offshore wind turbine prototype, an identification technology based on optimized spectral kurtosis (OSK) and the ensemble empirical mode decomposition (EEMD) methods, which is capable of determining both the structural vibration sources and the corresponding energy distribution was presented for the first time. It can be demonstrated that the structural vibration sources changed from environmental excitations to forced harmonic loads when the rotating speed and power of the turbine increased. At the same time, the main vibration energy underwent a transformation from random vibration energy to harmonic vibration energy with a change in the operational factors. Furthermore, it is suggested that the relationship between the rotating frequency and structural modal frequencies in the structural operational safety assessment of offshore wind turbines should be further investigated.


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  • Accession Number: 01690858
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Dec 14 2018 3:08PM