Unscented H-infinity Wind Speed Estimation and H-infinity Control of Wind Turbines
Bibliographic record
Abstract
Wind energy is a fast-growing industry in Canada and worldwide. As wind turbine size and capacity increase, control systems become exceedingly important to maximize the efficiency of the power output and to reduce loads to extend their longevity. This thesis aims to provide better knowledge of the input wind speed and to design turbine control based on this normally unknown input. First, non-linear robust methods of state estimation are introduced in order to deal with the nonlinearities present in the wind turbine model and the large exogenous disturbance of wind speed. Specifically, the unscented Kalman filter and an algorithm for the unscented H-infinity filter and their variants are analyzed in a case study for robustness and accuracy. An augmented unscented H-infinity scheme is then adopted in combination with a data fusion technique to estimate effective wind speed (EWS). This technique utilizes high frequency data from the anemometer and treats the turbine as a sensor to fuse them as one EWS measurement. The EWS is used to determine the optimal tip speed ratio (TSR) for the reference for the turbine. This is accomplished with a mixed sensitivity H-infinity tracking controller to optimize power output. The results show up to 15% improvement from the baseline controller for a 5 MW turbine and consistently high power output for a 1.5 MW turbine.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 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".