Integrated Control of Floating Offshore Wind Farms with Reconfigurable Layouts
Bibliographic record
Abstract
Abstract. This study proposes an integrated control method for floating offshore wind farms (FOWFs) that seeks to maximize farm-level power output or regulate it to a prescribed reference while mitigating wake-induced losses. To achieve these objectives, the method integrates existing control strategies: turbine repositioning, wake steering, power derating, and dynamic wake mixing, within a unified framework that adaptively selects the most effective combination based on wind conditions and control goals. This integration is motivated by the fact that individual strategies may be effective only under specific conditions or broadly effective but not always optimal, whereas their coordinated use can deliver robust performance improvements across a broad range of operating scenarios. The framework targets FOWFs with reconfigurable layouts, where turbines are mounted on floating platforms anchored to the seabed with sufficiently long and slack mooring lines, allowing them to shift within a certain range and thereby enabling controlled positional adjustments. Numerical simulations using the flow redirection and induction in steady state (FLORIS) engineering wake model show that the integrated method consistently outperforms any individual strategy. These findings highlight the potential of integrated control to enhance the efficiency, flexibility, and adaptability of FOWFs, offering a promising pathway to overcome the limitations and improve the performance of standalone control methods.
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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) | 0.001 | 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) | 0.001 | 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".