DFIG wind turbine under unbalanced power system conditions using adaptive fuzzy virtual inertia controller
Bibliographic record
Abstract
The Doubly-Fed Induction Generator (DFIG) based Wind Turbines Generator (WTG) with traditional Maximum Power Point Tracking (MPPT) control provides no inertia response under system frequency events. Recently, the DFIG wind turbines have been equipped with the Virtual Inertia Controller (VIC) to enhance the frequency stability of the power system. However, the conventional VICs with fixed gain have negative effects on the inter-area oscillations of regional networks. To cope with this drawback, this paper proposes a novel adaptive VIC to improve both the inter-area oscillations and frequency stability. In the proposed scheme, the gain of the VIC is dynamically adjusted using fuzzy logic. The effectiveness and control performance of the adaptive fuzzy VIC is evaluated under different frequency events such as loss of generation and three-phase fault with load shedding. The simulation studies are performed on a generic two-area network integrated with a DFIG wind farm, and the comparative results are presented for these three cases: DFIG without VIC, DFIG with fixed gain VIC, and DFIG with adaptive fuzzy VIC. The results confirm the ability of the proposed adaptive fuzzy VIC in improving both the interarea oscillations and frequency stability of the 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 | 0.000 |
| 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".