Rotation Speed and Wind Speed Indirect Measurement Methods for the Control of Windmills with Fixed Blades Turbine
Bibliographic record
Abstract
The implementation complexity of classical algorithms for wind turbines control limits the utilization of low-end microcontrollers for such applications. We conceived an original method that aims to optimize the efficiency of windmills with fixed blades with the variation of the wind speed. In the paper we present an original method to determine generator's efficiency values using measurements that were performed at the prototype's homologation, along with original simplified models of wind turbine and of wind turbine - generator assembly. Measured values of the frequency, voltage and current of the generator are applied to determine the turbine rotation speed and the wind speed, reducing thus the number of needed sensors. A couple of families of regression functions were derived and tuned accordingly to the factory experimental characteristics to allow for a facile calculation of the generator's efficiency database table. To further speedup the assessment of the optimum /safety value of the windmill rotation speed, the generator's model is considered to provide the rotation as a function of the wind speed in a tabular format that is further used at run-time as a look-table for rapid interpolations in the control loop. The main advantages of the proposed approach are the simplicity and robustness of the implementation, being easily portable to low-end microcontroller platforms.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".