A study of modal damping for offshore wind turbines considering soil properties and foundation types. Issue 12 (9th August 2019)

• Record Type: Journal Article
• Authors: Ishihara, Takeshi; Wang, Lilin
• Is Part Of: Wind energy. Volume 22:Issue 12(2019)
• Page Start: 1760
• Page End: 1778
• Publication Date: 2019-08-09
• Subjects: GA based identification -- gravity foundation -- modal damping -- monopile foundation -- Offshore wind turbine -- soil properties; Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.2401
• Languages: English
• ISSNs: 1095-4244

Abstract

Abstract: The modal damping ratio for each mode is crucial to characterize the dynamic behavior of offshore wind turbines and widely used by simulation software in wind turbine engineering, such as Bladed and FAST. In this study, modal damping ratios of offshore wind turbines are systematically studied for different soil properties and foundation types. Firstly, the modal damping ratios and modal frequencies for the first and second modes of a gravity foundation–supported offshore wind turbine are studied. An offshore wind turbine supported by a monopile foundation is then investigated to clarify the characteristics of modal damping ratios and modal frequencies for the monopile foundation. The soil parameters are identified by means of genetic algorithm (GA). Predicted modal damping ratios and modal frequencies as well as modal shapes show good agreement with the field measurements for both foundations. Finally, a sensitivity analysis study is carried out to investigate the effects of soil properties and foundation types on modal damping ratios. For the gravity foundation–supported offshore wind turbine, soil properties affect the modal damping ratio of the second mode largely, but affect that of the first mode little, while for the monopile‐supported offshore wind turbine, soil properties affect the modal damping ratios of the first and second modes significantly. Predicted natural periods and modal damping ratios of the first mode for both foundations by a pair of simple models agree well with those by numerical models.