Optimal torque control based on effective tracking range for maximum power point tracking of wind turbines under varying wind conditions
Bibliographic record
Abstract
This study focuses on the development of optimal torque (OT) control, which is a commonly used method for maximum power point tracking (MPPT). Due to the sluggish response of wind turbines with high inertia, conventional OT control was improved to increase MPPT efficiency by dynamically modifying the generator torque versus rotor speed curve. An idea that tracking a local interval of wind speed where the wind energy is primarily distributed rather than the total range of wind speed variation is applied in this study. On this basis, an effective tracking range (ETR) that corresponds to the local interval of wind speed with concentrated wind energy distribution is proposed and an improved OT control based on ETR is developed. In this method, based on a direct relationship between ETR and wind conditions, the torque curve can be quickly optimised so that higher and more stable MPPT efficiency can be achieved under varying wind conditions. Meanwhile, MPPT efficiency enhancement by reducing tracking range without increasing torque discrepancy leads to a low cost of generator torque fluctuation and drive train load. Finally, simulations based on fatigue, aerodynamics, structures, and turbulence (FAST) code and experiments conducted on a wind turbine simulator are presented to verify the proposed method.
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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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 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".