On the Use of Energy Storage Systems and Linear Feedback Optimal Control for Transient Stability
Bibliographic record
Abstract
In this paper, we study a distributed control strategy that harnesses the highly granular data available in future power systems in order to improve system resilience to disturbances. Specifically, we investigate the role of external energy storage systems (ESSs) in stabilizing the dynamics of power systems during periods of disruption. We consider an information-rich multiagent framework and focus on ESS output control via linear feedback optimal (LFO) control to achieve transient stability. The LFO control scheme relies on receiving timely state information to actuate distributed ESSs in order to drive the synchronous generators to stability. We evaluate the performance of the LFO control on the 39-bus 10-generator New England test power system in the presence of ideal and nonideal conditions including communication latency, finite sampling rate, and sensor noise. The LFO controller is found to have a simple structure, be tunable, and to have fast response to achieving transient stability while being sensitive to information latency and data rate.
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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 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".