Load measurement and parameter adaptation technologies for floating offshore wind turbines

Accurately assessing the loads of the floating offshore wind turbines (FOWTs) in complex marine environments is crucial for safety control and fatigue life assessment. At present, there is a significant gap in the field of accurately measuring or identifying the wind loads of FOWTs. To address this problem, load measurement and parameter adaptation technologies were proposed in this paper. Firstly, an identification model was built to fulfill the load measurement through platform motion signals. And a machine learning model was trained and applied to update the parameters of the identification model. Then the parameter adaptation method was formed by integrating the two models which have different strengths and weaknesses. To validate the proposed method, the 10 MW FOWT simulation based on OpenFAST was carried out. The root mean square error (RMSE) of the thrust measurement was significantly increased to 16.06% when the system parameter changed, and then reduced to 9.39% after parameter adaptation. Finally, the scaled model tests with the software-in-loop were conducted in the wave basin. Experimental results also confirmed the effectiveness of the proposed load measurement and parameter adaptation technologies.