A Self-Regulating Virtual Synchronous Generator Control of Doubly Fed Induction Generator-Wind Farms
Bibliographic record
Abstract
The inverter-driven renewable energy sources (RESs), such as wind energy conversion systems (WECS), pose major threats toward system stability due to lack of inertia. Hence, virtual inertia concepts have gained popularity, for control and improvisation of the dynamic behavior of RESs, by simulating the kinetic inertia of the synchronous generator. This article focuses on developing an improved self-regulating virtual synchronous generator (VSG) control for grid-tied doubly fed induction generator (DFIG)-wind farms (WFs). The proposed scheme provides frequency support to the system while ensuring the low-voltage ride through (LVRT) capability at transient conditions, as per grid code requirements (GCRs). This has been achieved by introducing an additional control at grid side converter (GSC). This auxiliary control consists of a combined approach of VSG control and a current limiting approach. The VSG loop that alters the inertia of the system improves the frequency of the system and the current limiting loop provides the required inductance to limit fault current. This overall loop uses a self-regulating approach, and the developed concept helps to suppress the transients in stator current. The study obtained on a multimachine system and also for a weak grid system confirms the effectiveness and viability of the modified converter control structure.
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) | 0.001 | 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".