Fatigue Load Minimization for a Position-Controlled Floating Offshore Wind Turbine
Bibliographic record
Abstract
Floating offshore wind farm control via real-time turbine repositioning has a potential in significantly enhancing the wind farm efficiency. Although the wind farm power capture increase by moving platforms with aerodynamic force has been verified in a recent study, the investigation and mitigation of the fatigue damage caused by such aerodynamic force manipulated for turbine repositioning is still necessary. To respond to these needs, this paper presents fatigue load controller design for a semisubmersible floating offshore wind turbine, particularly when the turbine position is controlled by the nacelle yaw angle. At various turbine positions determined by nacelle yaw angles and average wind speeds, the designed controller manipulates three blade pitch angles individually and minimizes the fatigue load at the tower base. As the individual blade pitch controller, the linear quadratic regulator is optimized through surrogate optimization by simulating the turbine disturbed by various turbulent wind and irregular wave profiles, and then by searching for a minimum fatigue from these simulations. Fatigue load analysis with the optimized controller leads to the main contribution of this paper, that is, to demonstrate that turbine repositioning can be achieved while allowing for the inclusion of a fatigue reducing controller. In fact, when operating the FOWT with the position controller and fatigue load controller, the fatigue damage at the tower base is reduced by about 40% for different nacelle yaw angles. This result supports the feasibility of position-controlled wind turbines to optimize the wind farm efficiency, thereby drastically reducing the offshore wind energy cost.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".