Low Voltage Ride-Through protection techniques for DFIG wind generator
Bibliographic record
Abstract
Due to the rapid increase of penetration level of wind generation connected directly to the bulk power system grid, a new grid codes have been issued that require Low-Voltage Ride-Through (LVRT) capability for wind turbines so they can remain online and support the electric grid post fault events instead of instantaneous tripping. This capability will increase the stability of the network and reduce generation shortage after the fault clearance. Each utility has its own grid codes for this LVRT. There are many types of wind generators, and currently the Doubly Fed Induction Generator (DFIG) is the most popular type among the leading wind turbine (WT) manufacturers. In this paper five LVRT methods for protection of DFIG during LV events are implemented and compared. The five methods are Crowbar, DC Chopper, series dynamic resistances, and two hybrid methods that combine DC chopper with Crowbar and DC chopper with series dynamic resistances respectively. These methods were tested under different types of fault including symmetrical and unsymmetrical faults and their performances were compared.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".