A systematic approach to delay-adaptive control design for smart grids
Bibliographic record
Abstract
Communication latency poses a challenge to the operation of control systems in power systems. Specifically, excessive delay between sensors and controllers can substantially worsen the performance of distributed control schemes. In this paper we utilize the parametric feedback linearization (PFL) control to actuate energy storage systems in order to achieve transient stability. A delay-adaptive design capitalizing on the features of PFL control is presented in this work in order to enhance the time delay tolerance of the power system. Specifically, a piece-wise linear delay-adaptive property of the PFL control is investigated. The parameters of the PFL controller are adapted according to the latency value in the cyber component of the grid in order to optimize performance. The enhancements achieved by the proposed delay-adaptive scheme on the performance of the distributed controller are shown when applied to the New England 39-bus and WECC 9-bus power systems following the occurrence of a physical disturbance. Further, numerical results show that the proposed delay-adaptive control is able to tolerate substantial delay without degradation in performance.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".