Application of an Augmented Unscented H-infinity Effective Wind Speed Estimation to 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 in order to maximize the efficiency of the power output and to reduce loads to extend their longevity. Effective wind speed (EWS) is not easy to measure because wind speed varies in time and space across the rotor area of the turbine, nor is it a real physical signal. This research builds on a previous paper’s method of EWS estimation to design a turbine controller based on this normally unknown input. The unscented H-infinity scheme was used in combination with a data fusion technique to estimate effective wind speed. The EWS was used to determine the optimal tip speed ratio (TSR) for the reference for the turbine. This was accomplished with a mixed sensitivity H-infinity tracking controller to optimize power output. The mixed sensitivity control was used to limit the bandwidth of the controller while also minimizing the tracking error. The methods are thus shown to be effective in wind speed estimation for two sizes of turbines and showed up to a 14.6 percent increase in power production the in maximum power point tracking region when compared to a baseline PI controller.
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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 | 0.001 |
| 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 | 0.000 | 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".