SIMULATION ANALYSIS OF DFIG CHARACTERISTICS UNDER d–q CONTROL STRATEGIES IN STATOR-VOLTAGE-ORIENTED FRAME
Bibliographic record
Abstract
The technology employed for electromechanical energy conversion in large wind turbines deviates from that used in traditional generation equipment. Induction machines, rather than synchronous generators, are used in most commercial wind turbines. The doubly-fed induction generator (DFIG) is a special variable-speed induction machine widely utilized in modern large wind turbines. Unlike a conventional fixed-speed induction machine, a DFIG transfers power to the grid through both the stator and rotor paths, and its characteristics depend strongly on how the generator is controlled. To enhance control design and analysis, this paper investigates DFIG steady-state characteristics through computer simulation. The paper presents a steady-state model that allows (1) DFIG characteristic study under decoupled d-q control strategies in a stator-voltage-oriented frame and (2) power computation of both the stator and rotor paths. The steady-state model is validated through a transient simulation system by using SimPowerSystem. Extensive simulation-based studies are performed to investigate how DFIG characteristics are affected by different d-q control conditions in the stator-voltage-oriented frame.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".