Predicting Hub-Height Wind Speed for Small Wind Turbine Performance Evaluation Using Tower-Mounted Cup Anemometers
Bibliographic record
Abstract
Industry standards for small wind turbine (SWT) performance evaluation require estimating hub-height wind speed using either a spatially offset meteorological mast or a cup anemometer extending from a lower elevation on the turbine tower. This paper investigates the use of vertical extrapolation to reduce the uncertainty associated with tower-mounted anemometer wind speed measurements. An experimental study has been performed involving a Bergey XL.1 SWT collocated with a meteorological mast. Results indicate that power law extrapolation can significantly reduce the uncertainty of hub-height wind speed predictions, especially if concurrent wind speed measurements are available at multiple elevations. Best practice methods have been provided. To identify the upper limit of anemometer placement, a porous disk wind tunnel test has been performed and compared with three-dimensional wind speed measurements obtained experimentally. To remain outside the rotor's region of influence, it is recommended that the topmost anemometer is positioned one rotor diameter below hub-height.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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".