Damping low-frequency oscillations by tuning the operating point of a dc-segmented ac system
Bibliographic record
Abstract
This paper introduces and evaluates a new method for the small-signal stability enhancement of large ac power systems based on segmentation through line-commutated HVDC links. The proposed method is based on the fact that the oscillatory modes of the system may vary by changing the system operating point. In this study, the system operating point is varied by rerouting the flow of power in the ac transmission lines. The flexibility of the HVDC lines in controlling the flow of power in a dc-segmented ac system is used to control the power transmission in the ac lines. An optimization problem is proposed to determine the optimum set-points of the HVDC lines to increase the damping ratio of the underdamped oscillatory modes. Simulation results show that at the optimum operating point, the low-frequency oscillatory modes are damped out significantly and the system net oscillation is less than that of the system at the operating point obtained from a standard economic dispatch. Simulation results also show that there is a trade-off between the generation cost and damping ratio and the proposed method effectively enhances the small-signal stability for a variety of system configurations.
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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".