Nonlinear control of wind energy conversion system based on Control-Lyapunov function
Bibliographic record
Abstract
Wind Energy Conversion Systems (WECS) require a control system which is able to maintain the stability of the power conditioning system under a wide range of operating conditions. Conventional PI-based dq-controllers are very difficult to stabilize, especially under a wide range of operating conditions and in the presence of un-modeled dynamics and uncertainties. This paper introduces a novel nonlinear controller based on Control-Lyapunov Functions (CLF) providing robust and reliable closed-loop control. The proposed CLF-based controller offers some attractive features for this particular application. The controller is able to track the reference point with less effort due to the fact that it does not remove the inherent self-stabilizing terms. Also, due to the inclusion of integral terms in the control system, the sensitivity to parameter variations is vastly reduced since the Lyapunov function compensates itself by accumulating errors. Lastly, the stability of the closed-loop is also guaranteed for all operating conditions. Experimental results show good performance of the closed-loop control system even under severe load changes.
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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) | 0.000 | 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) | 0.001 | 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".