Disturbance Margin for Quantifying Limits on Power Smoothing by Wind Turbines
Bibliographic record
Abstract
Wind turbines can, in principle, be operated to smooth wind power fluctuations by allowing wider variations in turbine speed and generator torque to store and release energy. This ability must be constrained by turbine speed and generator torque limits. To present, work in the literature is conceptual and does not indicate what extent of smoothing is possible before component limits are reached, nor does it quantify sensitivity to variations in the input wind speed. This paper introduces a method for quantifying how much wind variation a wind turbine can absorb in variable speed mode while still being guaranteed to operate within its component limits. One can apply this method to obtain the dependence of maximum tolerable wind disturbance on the smoothing time constant, and thus make design decisions. This paper shows that the analysis of torque speed intersections, as standardly applied in electric machine theory, is of limited use for studying power smoothing. The new conclusions and design choices made available by the proposed method are illustrated with a series of computation examples. The method is shown to agree asymptotically with two limiting cases that can be calculated based on torque-intersection analysis. The method is based on new theory for computing invariance kernels for nonlinear planar systems, and can be adapted to assess the robustness of other control laws.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".