Ensuring the operational safety of offshore wind turbine (OWT) structures during their service period requires accurate identification on the operational modal parameters (OMPs), which are not only a crucial parameter which reflect the structure’s vibration characteristics, but also a key index for evaluating the structural healthy status. However, due to the complex and unpredictable ocean environmental circumstances, the measured signals obtained from the actual OWT structures are frequently accompanied by a huge amount of low-frequency, high-energy noise, which has a significant influence on the identification accuracy of OMPs. Therefore, one called CSVS (CEEMDAN-SSA-VMD-SSI) modal identification process, which combined the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), sparrow search algorithm (SSA), variational modal decomposition (VMD) and stochastic subspace identification (SSI) method, was proposed for identifying modal parameters of OWT structures under operational conditions. It aims to mitigate the influence on the identification accuracy resulted from the low-frequency, high-energy noise and investigates the variations of modal parameters based on measured data. Firstly, the CEEMDAN method and VMD process optimized by the SSA were used to decompose the signal and remove the low-frequency, high-energy noises, and then the SSI method was following applied to identify and extract the OMPs from the measured data. Secondly, the efficiency of the proposed CSVS approach to identify OMPs of one 3.3 MW OWT operating in Yellow sea of China, was confirmed based on the measured vibration displacement signals under various operational conditions by comparing the results identified from the classic method. Finally, the distribution characteristics of the natural modal frequency, impeller rotation frequency (1P) and blade sweeping frequency (3P) were furtherly investigated, and the change regulations of identified OMPs with the operational factors including wind speed and rotational speed were also provided. It is indicated that the CSVS method shows the strong resistance to modal aliasing and effectiveness on noise reduction compared to the traditional methods so that it can accurately identify and distinguish the natural modal frequency, 1P frequency and 3P frequency of the OWT structure. Further, it may provide the essential technical support for identifying the OMPs and evaluating the operational safety of OWT structures.
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