Utilization of blade pitch control in low wind speed for floating offshore wind turbines
Bibliographic record
Abstract
This paper investigates a potential advantage of utilizing blade pitch control over the conventional fixed blade pitch strategy in low wind speed for horizontal-axis offshore floating wind turbines mounted on a barge platform. To examine the advantage, simulation studies with a 5MW wind turbine model in the software FAST are conducted. The generated power and the platform pitch movement are compared among closed-loop systems with three feedback controllers, that is, a baseline controller with fixed blade pitch, a linear-parameter-varying (LPV) controller with fixed blade pitch, and an LPV controller with varying blade pitch. The LPV controllers are gain-scheduled in terms of wind speed. For the design of LPV controllers, an LPV model which represents a family of linearized models of the nonlinear model in FAST over the low wind speed range is employed, and a well-known LPV controller design technique is applied to the LPV model. Simulation results demonstrate that the utilization of blade pitch control can reduce the platform pitch oscillation by more than 5 percent compared to fixed blade pitch strategies, possibly by slight reduction in power capture. This suggests the usage of blade pitch control in low wind speed when the cost decrease due to the load reduction outweighs the cost increase caused by the loss of power generation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".