A new control strategy to suppress the tower vibrations of variable speed wind turbines
Bibliographic record
Abstract
In this paper, an adaptive internal model based controller is presented for suppressing the tower vibrations in variable speed wind turbines. Wind turbines have a dedicated current and power control module for the second operation region of the machine, where the machine works below the rated wind speed, and a pitch controller for the third operation region of the machine, where the machine works above the rated wind speed. The current and power controllers are designed to maximize the captured power below the rated wind speed, whereas the pitch controller is designed to limit the rotation speed and generated power above the rated wind speed. The pitch controller can further be used to suppress the mechanical tower vibration. Since the vibration frequencies of a tower are not exactly specified in the real turbines, the proposed method, which is based on the behavior of an internal model in an error feedback system, utilizes an algorithm to identify the vibration frequency followed by cancelling the vibration signal. Four different software packages including FAST, TurbSim, AeroDyn, and Simulink are integrated and used for simulation studies. Furthermore, performance of the controller is investigated in normal and fault ride through conditions. The results indicate accurate and proper performance of the controller in terms of identification of the tower vibration frequency and its suppression.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".