Fault ride through capability of double fed induction generator for wind energy system
Bibliographic record
Abstract
Until recently it has been accepted that induction generator based wind turbines are disconnected from the power system in the event of a network disturbance. However, the increasing trend of connecting high penetrations of wind farms to transmission networks has resulted in the transmission system operators revising their grid codes for the connection of large MW capacity wind farms. The new grid codes require wind turbines to remain connected for a specified voltage disturbance on the network. Most of the wind generation plant being developed will use either fixed speed induction generator (FSIG) or doubly fed induction generator (DFIG) based wind turbines. The basics of using a doubly-fed induction generator (DFIG) to convert the mechanical energy of the wind into useful electrical power that can be used to supply electricity to any grid are presented. The ability of doubly fed induction generator based wind turbines to remain connected through power system disturbances is discussed. A crowbar protection system to provide a power system fault ride-through capability for doubly fed induction generator based wind turbines is also described. The dynamic behaviour ofDFIG wind turbines normal operation and during grid faults are simulated and assessed to verify the recommended method using a Matlab/Simulink developed model.
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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".