Fast-frequency response from wind generators ‒ Empirical data from a Type 4 wind farm
Bibliographic record
Abstract
Grid stability is an important consideration as inverter-based generators replace synchronous generators. Primary frequency response is one part of grid stability and counters rapid grid frequency changes. Synchronous generators achieve this via electromechanical coupling between the machine stator and the mechanical inertia of the rotor. Advances in power converter technology enable inverter-based generators to contribute to stability in a similar ‒ although not identical manner and provide fast-frequency response that is a component of primary frequency response. An important benefit is that converters provide a tunable response ‒ something not possible with synchronous generators. Wind turbines with full converters are able to extract some rotational kinetic energy from their spinning rotors and deliver it to the grid as electrical energy. This work documents and analyses the ability of full-converter wind turbines to provide fast-frequency response. We use a 50.6 MW, transmission connected wind farm and inject an artificial frequency signal with a sudden frequency dip. The wind farm remains connected to the grid as the fast-frequency response is triggered and rotor kinetic energy is converted into electrical energy. We repeat this at different power levels, gather high-frequency data and measure parameters such as response rise time, peak power boost and injected energy. We find that the resulting response aligns well with expectations although prevailing wind speeds influence performance in some instances.
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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 | 0.002 |
| 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 | 0.001 | 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".