Frequency Control Support of a Doubly-Fed Induction Generator Based on the Torque Limit
Bibliographic record
Abstract
This paper proposes a torque limit-based inertial control scheme of a doubly-fed induction generator (DFIG) that supports the frequency control of a power system. If a frequency deviation occurs, the proposed scheme aims to release a large amount of kinetic energy (KE) stored in the rotating masses of a DFIG to raise the frequency nadir (FN). Upon detecting the event, the scheme instantly increases its output to the torque limit and then reduces the output with the rotor speed so that it converges to the stable operating range. To restore the rotor speed while causing a small second frequency dip (SFD), after the rotor speed converges the power reference is reduced by a small amount and maintained until it meets the reference for maximum power point tracking control. The test results demonstrate that the scheme can improve the FN and maximum rate of change of frequency while causing a small SFD in any wind conditions and in a power system that has a high penetration of wind power, and thus the scheme helps maintain the required level of system reliability. The scheme releases the KE from 2.9 times to 3.7 times the Hydro-Québec requirement depending on the power reference.
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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) | 0.000 | 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 | 0.001 | 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".