Data-Driven Fault Recovery With Software-Defined Smart Transmission Grids
Bibliographic record
Abstract
This study presents a Software-Defined Transmission Grid (SDTG) framework, integrated with digital and cyber-physical systems, to enable data-driven control within a smart transmission grid. The framework aims to improve grid reliability and accelerate fault recovery. We utilized the Schur complement network reduction technique in the digital model of SDTG to improve grid control through data-driven strategies, with a specific emphasis on rapid system fault recovery. Mathematical proofs based on classic circuit and algebraic graph theories were presented to confirm the effectiveness of the reduced network. Additionally, we introduced a data-driven optimal control approach and demonstrated that the optimal control input can be obtained from a finite set of past control signals, even in the absence of explicit knowledge regarding fault types or the grid’s dynamic response. The proposed methodology enhances grid management and offers a flexible and scalable framework for post-fault operation and grid restoration, serving as a foundational approach in other complex transmission grid management scenarios. The effectiveness of this approach was validated through numerical case studies.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".