Offshore and onshore ground-generation airborne wind energypower curve characterization
Bibliographic record
Abstract
Abstract. Airborne wind energy systems (AWESs) aim to operate at altitudes well above conventional wind turbines (WTs) and harvest energy from stronger winds aloft. While multiple AWES concepts compete for entry into the market, this study focuses on ground-generation AWES. Various companies and researchers proposed power curve characterizations for AWES, but no consensus for an industry-wide standard has been reached. An universal description of a ground-generation AWES power curve is difficult to define because of complex tether and drag losses as well as alternating flight paths over changing wind conditions with altitude, as compared to conventional WT with winds at fixed hub height and rotor area normalization. Therefore, this study determines AWES power and annual energy prediction (AEP) based on the awenox optimal control model for two AWES sizes, driven by representative 10-minute onshore and offshore mesoscale WRF wind data. The wind resource is analyzed with respect to atmospheric stability as well as annual and diurnal variation. The wind data is categorized using k-means clustering, to reduce the computational cost. The impact of changing wind conditions on AWES trajectory and power cycle is investigated. Optimal operating heights are below 400 m onshore and below 200 m offshore. Efforts are made to derive AWES power coefficients similar to conventional WT to enable a simple power and AEP estimation for a given site and system. This AWES power coefficient decreases up to rated power due to the increasing tether length with wind speed and the accompanying tether losses. A comparison between different AEP estimation methods shows that a low number of clusters with three representative wind profiles within the clusters yields the highest AEP, as other wind models average out high wind speeds which are responsible for a high percentage of the overall AEP.
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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 | 0.000 | 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".