Control Methodology to Mitigate the Grid Impact of Wind Turbines
Bibliographic record
Abstract
This paper introduces a new control topology for converter-interfaced wind turbines. Through a singular perturbation decomposition of the system dynamics, a controller is designed that isolates wind-power fluctuations from the power grid. Specifically, the controller causes the closed-loop wind turbine to behave as a simple first-order power filter, where power injected into the grid is a low-pass filtered version of the incident wind power. It is shown that a turbine hub-speed instability imposes a limit on the largest filtering time constant that may be safely implemented. A linearized analysis is used to calculate how a small filter time constant can be implemented to obtain regulation of the tip-speed ratio for the widest range of frequencies. The methodology thus offers the possibility to either deliver a filtered power at suboptimal conversion efficiency or track peak wind power. It is mathematically demonstrated that the control structure achieves the regulation of torsional dynamics and the dc-link capacitor voltage without involving the grid-side converter controls, thus eliminating the influence of those dynamics on the grid. Simulation studies are used to demonstrate the methodology's viability and explore the associated tradeoffs.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".