Grid-Forming Control of DFIG-Based Wind Turbine Generator by Using Internal Voltage Vectors for Asymmetrical Fault Ride-Through
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Bibliographic record
Abstract
Grid-forming (GFM) controls exhibit robust frequency and voltage support capabilities for inverter-based resources (IBRs). This also shows promise for doubly fed induction generator-based wind turbine generators (DFIG-based WTGs). However, during asymmetrical faults, the DFIG-based WTG that employs GFM controls (GFM-DFIG) might suffer from overcurrent and overmodulation of the rotor-side converter (RSC). Therefore, from the perspective of positive-sequence, negative-sequence, and transient components, this paper proposes asymmetrical fault ride-through (FRT) controls for the GFM-DFIG based on the mechanism for forming the grid voltage. Firstly, internal voltage vectors are designed for the assessment of asymmetrical FRT capabilities. Then a positive- and negative-sequence control (PNSC) is proposed to support the sequence components of internal voltage vectors for the GFM-DFIG. On this basis, an asymmetrical FRT control structure is proposed, incorporating negative-sequence reactive current injection and two types of positive-sequence control schemes: the current saturation-based method and virtual impedance-based method. Additionally, a simplified calculation method for transient voltages is utilized to eliminate the impacts of transient flux leakage. Finally, the proposed FRT controls for the GFM-DFIG are validated by using the EPRI benchmark system. The results indicate that, with the proposed control, GFM-DFIG can maintain stable voltages and achieve required negative-sequence behaviors.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it