Some Effects of Efficiency on Wind Turbine Interference and Maximum Power Production
Bibliographic record
Abstract
The effects of finite efficiency on wake interference and power output are studied for identical horizontal-axis wind turbines in line with the wind. Interference occurs whenever turbines reduce the power available to any turbines downwind of them. The analysis uses wake models that are commonly employed in wind farm layout design. The aerodynamic efficiency is varied from 75% to 100% for six identical hypothetical turbines in line parallel to the wind direction with spacing from four to six rotor diameters. Selfish optimization of the power output of individual turbines and co-operative optimization of the total power are considered. For selfish control, all turbines operate at the maximum possible local C P, based on the aerodynamic efficiency. For co-operative optimization, each turbine operates at a C P value between zero and the selfish value, and the total power is optimized numerically. The results show that at low efficiency, there is little difference in total power output between the two strategies. As efficiency increases, co-operative optimization produces increasingly more power. As turbine spacing increases, the difference between the strategies decreases. There is, however, a realistic range of efficiencies and spacing over which more power is delivered by co-operative optimization. The simplest form of co-operative optimization requires reducing the power output of the most upwind turbine to allow increased performance from those downwind. This should be an easy strategy to implement in wind farm control.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".