Stability Enhancement of a DC-Segmented AC Power System
Bibliographic record
Abstract
This paper proposes and investigates a new line-commutated current-sourced converter (LCC)-high voltage dc current (HVDC) global supplementary control (GSC) strategy for stabilizing and enhancing the dynamic performance of a large ac system that is segmented by LCC-HVDC links. The GSC is designed based on a linear quadratic Gaussian (LQG) method and enables coordinated supplementary control action of multiple HVDC links that participate in segmentation. The GSC can stabilize the ac system while either minimizing the propagation of oscillatory dynamics from one segment to other segments (GSC1) or enabling their controlled transfer from a disturbed segment to other segments (GSC2). The study results show that in a fully-dc-segmented system: 1) GSC1 and GSC2 are able to stabilize the system; 2) under GSC1, each segment can experience major disturbances without causing adjacent segments to experience the disturbances with the same degree of severity; and 3) GSC2 enables controlled transfer of the oscillations among the segments and, depending on the system configuration, can further reduce the magnitude and duration of oscillatory dynamics. The studies are conducted on a three-segment ac system including three interconnecting LCC-HVDC links.
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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".