Decentralized Supplementary Control of Multiple LCC-HVDC Links
Bibliographic record
Abstract
This paper presents a decentralized wide-area coordinated supplementary control of multiple line-commutated converter (LCC)-HVDC links to 1) prevent interactions among the HVDC links and 2) enhance the damping of the inter-area oscillatory modes. The proposed approach is based on the sparsity-promoting optimal control. The main features of the proposed approach are 1) it requires minimal communication infrastructure to achieve the control objectives and thus reduces the impacts of communication delays and noise, 2) it entails in an optimal gain which preserves the closed-loop stability and 3) it does not require the estimates of the system states. The performance of the proposed controller is evaluated based on eigen analysis and time-domain simulation of an interconnected AC system that includes five LCC-HVDC links. Performance of the proposed controller is also compared with those of fully centralized and conventional local supplementary controllers and its merits are highlighted. The studies indicate the proposed controller, based on 13 remotely communicated signals, provides similar performance as that of a fully centralized optimal controller using 2050 communicated signals and is far superior to the conventional local supplementary controllers.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".