Optimal Wind Power Plant Layout Using Ant Colony Optimization
Bibliographic record
Abstract
The optimal layout of wind power plants is very important as the arrangement of wind turbine generators (WTGs) profoundly affects the overall energy output of the wind power plant. To address this important issue, this research investigates the best layout for WTGs in wind power plants with different terrain features across three locations in Thailand using the Ant Colony Optimization (ACO) algorithm. The objective functions of maximizing the net annual energy production (AEP) and minimizing the wake losses were used to achieve the optimal wind power plant layout. Using the MERRA-2 database, computational fluid dynamics (CFD) wind flow modeling was performed to create 10x10 km2 microscale wind resource maps of locations characterized by flat, semi-complex, and complex terrains to install wind power plants. The CFD wind flow modeling yielded wind speeds of 5.00 to 5.76, 4.21 to 8.90, and 3.10 to 4.45 m/s for the flat, semi-complex, and complex terrains, respectively, making them feasible for utility-scale wind power plants. WTGs of multiple blade diameters, ranging from 90 to 126 m, with a nominal capacity of 2.5 MW at 100 m above ground-level hub heights, were used in this study. The Gamesa G126-2.5MW WTG with a 126 m blade diameter produces the highest net AEP of 14.3, 76.1, and 38.9 GWh/yr for the three terrains. Hence, this WTG was used to perform an ACO-based optimization to improve the electricity production of the wind power plants. Such studies are important to improve the efficiency of wind power plants, thus extracting the maximum kinetic energy possible from the winds and improving the economic viability of wind power plants.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".