Current status and grand challenges for small wind turbine technology
Bibliographic record
Abstract
Abstract. While modern wind turbines have become by far the largest rotating machines on Earth with further upscaling planned for the future, a renewed interest in small wind turbines is fostering energy transition and smart grid development. Small machines have traditionally not received the same level of aerodynamic refinement of their larger counterparts, resulting in lower efficiency, lower capacity factors, and therefore a higher cost of energy. In an effort to reduce this gap, research programmes are developing worldwide. With this background, the scope of the present study is twofold. In the first part of this paper, an overview of the current status of the technology is presented in terms of technical maturity, diffusion, and cost. The second part of the study proposes five grand challenges that are thought to be key to fostering the development of small wind turbine technology in the near future, i.e.: (1) improve energy conversion of modern SWTs through better design and control, especially in the case of turbulent wind; (2) better predict long-term turbine performance with limited resource measurements and prove reliability; (3) improve the economic viability of small wind energy; (4) facilitate the contribution of SWTs to the energy demand and electrical system integration; (5) foster engagement, social acceptance, and deployment for global distributed wind markets. To tackle these challenges, a series of unknowns and gaps are first identified and discussed. Based on them, improvement areas are suggested within which ten key enabling actions are finally proposed.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".