Dynamic performance assessment of offshore wind structures based on root morphology model.

Assessing the dynamic performance of offshore wind turbines (OWTs) is crucial for their safe operation and maintenance. To evaluate the structural dynamic performance, traditional methods rely on the reanalysis of damage models, leading to the lack of the real-time capability. Therefore, this paper presents a structural dynamic performance assessment technology based on the root morphology model to address such issue. The approach is developed to synergize data fusion-based and environmental condition classification-based dynamic performance assessment technologies. The data fusion-based method, using optimized VMD for denoising and ARMA models for free response signals, enables real-time dynamic performance assessment by tracking changes in AR coefficients over time. In parallel, the environmental condition classification-based method employs wind speed and wave height monitoring data to classify structural response data into multiple healthy sub-databases. Subsequently, Extenics is applied to compute the correlation between the data to be evaluated and the performance state levels based on the classified data, providing a precise evaluation of the structural dynamic performance. Finally, these two technologies are integrated through root morphology models, which fully consider structural vibration response data and environmental monitoring data. To verify the real-time and reliability of the proposed method, field study has been carried out on a 4 MW monopile OWT during the passage of Typhoon 'In-fa'. Results indicate that the root morphology model-based assessment technique effectively evaluates the impact of typhoons on the dynamic performance of OWTs. Further, a rose diagram for the dynamic performance assessment of OWTs is drawn to achieve real-time evaluation of OWT dynamic performance, which has certain engineering significance to guarantee the safe operation of OWTs and reduce the cost of operation and maintenance. • Root morphology model is developed to integrates different assessment outcomes. • Pearson distance is proposed to quantify the assessment result based on environmental condition classification. • Structural response data and environmental data are both considered in the assessment progress. • Field test has been carried out to verify the proposed method. [ABSTRACT FROM AUTHOR]