Multiple model predictive control for offshore wind turbines operating in the full‐load range
Bibliographic record
Abstract
Abstract This paper presents a multiple model predictive control (MMPC) strategy based on an individual blade pitch (IBP) mechanism to control floating offshore wind turbines (FOWTs) when operating in the full‐load range (above the rated wind speed). The strategy utilizes local model‐based predictive controllers (MPCs) to minimize generator output power fluctuations, generator speed variations, and alleviate tower mechanical loads, particularly fore‐aft and side‐to‐side shear forces. To ensure smooth transitions between local controllers, a soft‐switching technique is employed. The IBP actuation and generator torque control fix generator power and speed at the rated values, mitigating platform motions induced by wind and waves. The proposed control strategy is applied to a 5‐MW baseline wind turbine with a spar‐buoy platform developed by the National Renewable Energy Laboratory. Furthermore, a comparative analysis is conducted by designing an MMPC based on the collective blade pitch (CBP) mechanism. Through comprehensive simulation and analysis, the effectiveness of the IBP‐based MMPC strategy is demonstrated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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 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".