MR
M. Restrepo Botero
info
Please Note
<p>This page displays the records of the person named above and is not linked to a unique person identifier. This record may need to be merged to a profile.</p>
1 records found
1
Understanding the fatigue load history of wind turbines is critical for taking decisions regarding the lifetime of a project. However, direct measurement of fatigue loads at each turbine in a wind farm is unfeasible. For this reason, surrogate models offer a useful alternative. In this thesis, a methodology for creating surrogate models for emulating fatigue loads of offshore wind turbines is presented. The methodology is unique in that it accounts for the variability of site-specific conditions that may be present between wind turbines of the same class. First, a method for creating simplified structural models which depends only on a few degrees of freedom is derived. After this, a database of simulation data is assembled by varying the geometric, dynamic, and environmental degrees of freedom within ranges which capture a high degree of variability of possible site-specific conditions. This database is then used to train surrogate models using neural networks.
...
Understanding the fatigue load history of wind turbines is critical for taking decisions regarding the lifetime of a project. However, direct measurement of fatigue loads at each turbine in a wind farm is unfeasible. For this reason, surrogate models offer a useful alternative. In this thesis, a methodology for creating surrogate models for emulating fatigue loads of offshore wind turbines is presented. The methodology is unique in that it accounts for the variability of site-specific conditions that may be present between wind turbines of the same class. First, a method for creating simplified structural models which depends only on a few degrees of freedom is derived. After this, a database of simulation data is assembled by varying the geometric, dynamic, and environmental degrees of freedom within ranges which capture a high degree of variability of possible site-specific conditions. This database is then used to train surrogate models using neural networks.