Analysis and Damping of Mechanical Resonance of Wind Power Generators Contributing to Frequency Regulation
Bibliographic record
Abstract
Wind power generators will become increasingly useful in modern power systems; therefore, they should be able to support the system frequency in a way similar to that of conventional generators. Droop and virtual inertia are the most reported methods in the literature. However, their impact on the generator and system frequency stability, when the double-mass mechanical dynamics of a wind power generator are considered, has not been addressed thoroughly. In this paper, small-signal modeling, analysis, and eigenvalues studies are used to show that incorporating a wind power generator in the frequency regulation can expose its shaft to forces stimulating its natural resonance frequency dynamics and lead to instability. This paper shows that the mechanical resonance of frequency-regulating wind generators must be studied and enhanced not individually but as a part of the whole power system stability analyses. To overcome the stability problem caused by implementing frequency regulation in wind generators, this paper investigates different alternatives to stabilize the generator dynamics and, at the same time, minimize undesirable interference in the conventional wind power generator controllers. Time-domain simulation results validate the analytical results and discussions.
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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 | 0.001 |
| 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".