Wind Farm Layout Optimization Considering Commercial Turbine Selection and Hub Height Variation
Bibliographic record
Abstract
New aspects were added to the wind farm layout optimization problem; commercial turbine selection, generic realistic representation for the thrust coefficient, investigating the power-cost of energy trade-off range, and introducing the wind farm layout upgrade optimization problem. A range of commercial turbines was selected and the manufacturers’ power curves were used to evaluate the power developed by each turbine using the effective wind speed. The classical Jensen’s wake model was implemented to simulate the wake and wake interference within the farm in an analytic and accurate way. A simple field-based cost model was developed to evaluate the cost of any layout in terms of the turbine rated power and hub height. For the upgrade cases, the cost model included an area factor to account the upgraded area to the original farm area. A Genetic Algorithm was used for optimization throughout this dissertation. A technique called Random Independent Multi-Population Genetic Algorithm was used in some cases to accelerate the optimization. The results showed that co-operative optimization is superior over the selfish one. The turbine aerodynamic efficiency was found to magnify the difference between the two optimization strategies. A wide range of commercial turbines was selected and a useful range of power-cost of energy trade-off was obtained. The optimization was found to be more efficient in offshore cases because of the low entrainment coefficient in the wake model. The Random Independent Multi-Population technique caused a significant reduction in the speed of the optimization. It is likely that most farms can be efficiently and practically upgraded with a wide range of power-cost of energy trade-offs, using the proposed upgrade layout and the optimization objective.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".