A new three-dimensional wake model for the real wind farm layout optimization
Bibliographic record
Abstract
The utilization of wind energy has attracted extensive attentions in the last few decades around the world, providing a sustainable and clean source to generate electricity. It is a common phenomenon of wake interference among wind turbines and hence the optimization of wind farm layout is of great importance to improve the wind turbine yields. More specifically, the accuracy of the three-dimensional wake model is critical to the optiamal design of a real wind farm layout considering the combinatorial effect of wind turbine interaction and topography. In this paper, a novel learning-based three-dimensional wake model is proposed and subsequently validated by comparison to the high-fidelity wake simulation results. Moreover, due to the fact that the inevitable deviation of actual wind scenario from the anticipated one can greatly jeopardize the wind farm optimization outcome, the inaccuracy of wind condition prediction using the existing meteorologic data with limited-time measurement is incorporated into the optimization study. Different scenarios including short-, medium-, and long-term wind data are studied specifically with the wind speed/direction prediction errors of [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], [Formula: see text] 0.08 m/s, [Formula: see text] 1.75 [Formula: see text] and [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text], respectively. An advanced objective function which simultaneously maximizes the power output and minimizes the power variance is employed for the optimization study. Through comparison, it is found that the optimized wind farm layout yields over 210 kW more total power output on average than the existed wind farm layout, which verifies the effectiveness of the wind farm layout optimization tool. The results show that as the measurement time for predicting the wind condition gets longer, the total wind farm power output average increases while the error of power output prediction decreases. For the wind farm with 20 wind turbines installed, the individual power output is above 500 kW with an error of 90 kW under the short-term wind [Formula: see text] 0.25 m/s, [Formula: see text] 5.62 [Formula: see text], while it is above 530 kW with an error of 10 kW under the long-term wind [Formula: see text] 0.025 m/s, [Formula: see text] 0.56 [Formula: see text].
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".