Integrated Control of Floating Offshore Wind Farms with Reconfigurable Layouts
Bibliographic record
Abstract
Abstract. This paper proposes an integrated optimization-based control framework for floating offshore wind farms (FOWFs) with reconfigurable layouts. The framework coordinates four farm-level control strategies, that is, turbine repositioning, wake steering, power derating, and Helix wake mixing, to either (i) maximize total farm power output or (ii) track a prescribed farm-level power setpoint while mitigating wake effects. 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. Given the ambient inflow conditions (wind speed and direction), the framework computes coordinated per-turbine commands, including yaw angles, derating commands that limit each turbine's power to not exceed a prescribed value, and mean-to-peak amplitude of sinusoidal blade-pitch excitation, to meet the farm power requirement and to reduce the wake overlap. 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. However, because validation in the FLORIS model remains limited for cases in which yaw-based control, including turbine repositioning and wake steering, is applied simultaneously with Helix wake mixing or power derating on the same turbine, the corresponding quantitative gains should be interpreted with caution. We therefore also considered additional restricted benchmark cases in which these control actions were not assigned together to the same turbine. These cases provide conservative lower-bound benchmarks for the integrated-control studies, while still indicating that the main qualitative benefits of the proposed framework are preserved. 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| 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 | 0.001 | 0.001 |
| 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 source (direct Gemma or distilled Codex), 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".