Robust subsynchronous interaction damping controller for DFIG-based wind farms
Bibliographic record
Abstract
This paper proposes a robust controller to improve power system stability and mitigate subsynchronous interaction (SSI) between doubly-fed induction generator (DFIG)-based wind farms and series compensated transmission lines. A robust stability analysis is first carried out to show the impact of uncertainties on the SSI phenomenon. The uncertainties are mainly due to the changes in the power system impedance (e.g., transmission line outages) and the variations of wind farm operating conditions. Then, using the µ -synthesis technique, a robust SSI damping controller is designed and augmented to the DFIG control system to effectively damp the SSI oscillations. The output signals of the supplementary controller are dynamically limited to avoid saturating the converters and to provide DFIG with the desired fault-ride-through (FRT) operation during power system faults. The proposed controller is designed for a realistic test system with multiple series capacitor compensated lines. The frequency of the unstable SSI mode varies over a wide range due to the changes in power system topologies and wind farm operating conditions. The performance of the proposed controller is verified through electromagnetic transient (EMT) simulations using a detailed wind farm model. Simulation results also confirm the grid compliant operation of the DFIG.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".