Maximizing wind farm energy production in presence of aerodynamic interactions
Bibliographic record
Abstract
Wind energy is an attractive alternative to fossil fuels. However, as many other types of renewable energy sources, efficient control strategies development implies many challenges due to the dynamic and unpredictable behaviour of the energy source. More specifically, the variability of the wind velocity and the aerodynamic interactions between wind turbines reduce the electrical power production of wind farms. Extremumseeking control (ESC) is one way to reduce the power losses due to the wake effect in wind farm. In this paper, the multi-unit optimization (MUO) method has been used in order to maximize in real-time the extracted power of the wind farm of 6 wind turbines taking in consideration the wake effect. The use of MUO method is made possible by the definition of a novel objective function which considers a normalized power regardless of wind speed inputs. Simulation results show that using the MUO method lead to a fast convergence to the optimal operational point of the wind farm even in presence of a disturbed wind.
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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 | 0.000 | 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) | 0.000 | 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".