LVRT Capability of DFIG-Based WECS Under Asymmetrical Grid Fault Condition
Bibliographic record
Abstract
In this paper, the low-voltage ride-through (LVRT) capability of the doubly fed induction generator (DFIG)-based wind energy conversion system in the asymmetrical grid fault situation is analyzed, and the control scheme for the system is proposed to follow the requirements defined by the grid codes. As analyzed in the paper, the control efforts of the negative-sequence current are much higher than that of the positive-sequence current for the DFIG. As a result, the control capability of the DFIG restrained by the dc-link voltage will degenerate for the fault type with higher negative-sequence voltage component and 2φ fault turns out to be the most serious scenario for the LVRT problem. When the fault location is close to the grid connection point, the DFIG may be out of control resulting in non-ride-through zones. In the worst circumstance when LVRT can succeed, the maximal positive-sequence reactive current supplied by the DFIG is around 0.4 pu, which coordinates with the present grid code. Increasing the power rating of the rotor-side converter can improve the LVRT capability of the DFIG but induce additional costs. Based on the analysis, an LVRT scheme for the DFIG is also proposed by taking account of the code requirements and the control capability of the converters. As verified by the simulation and experimental results, the scheme can promise the DFIG to supply the defined positive-sequence reactive current to support the power grid and mitigate the oscillations in the generator torque and dc-link voltage, which improves the reliability of the wind farm and the power system.
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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) | 0.000 | 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 | 0.001 |
| Open science | 0.000 | 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".