Modeling and control of a small wind turbine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This paper starts with a detailed survey of control methods commonly employed by commercially available small wind turbines. This detailed survey indicates that the most commonly used control method of small wind turbines is horizontal furling method. Such furling mechanism and resulting dynamics are described in the paper. Furling is used to control the aerodynamic power extraction from the wind. A dynamic model of a small wind turbine with furling dynamics is presented in this paper. Such small wind turbines are based on permanent magnet generators and their speed can be regulated using the load control. The extraction of maximum power output from such wind turbines is investigated using tip speed ratio control and hill-climbing control methods. The system is simulated in Matlab/Simulink to determine a suitable control strategy. Two dynamic controllers are designed and simulated. In the first method, a controller uses the wind speed and rotor speed information and controls the load in order to operate the wind turbine at the optimum tip speed ratio. The generator output is observed in varying wind condition as the furl angle increases and decreases. In the second method, a controller compares the output power of the turbine with the previous power and based on the comparison it controls the load. Using a hill-climbing algorithm the controller tries to extract the maximum power from the wind, while the generator output is observed as the furl angle increase or decreases. Finally, the output of these two controllers is compared and investigated to determine which controller leads to the best results.
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.
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 it