Sensitivity factors based transmission network topology control for violation relief
Bibliographic record
Abstract
Transmission networks consist of thousands of branches for large‐scale real power systems. They are built with a high degree of redundancy for reliability concerns. Thus, it is very likely that there exist various network topologies that can deliver continuous power supply to consumers. The optimal transmission network topology could be very different for different system conditions. Transmission network topology control (TNTC) can provide the operator with an additional option to manage network congestion, reduce losses, relieve violation, and achieve cost‐saving. This study examines the benefits of TNTC in reducing post‐contingency overloads that are identified by real‐time contingency analysis (RTCA). The procedure of RTCA with TNTC is presented, and two algorithms are proposed to determine the candidate switching solutions. Both algorithms use available system data: sensitivity factors or shifting factors. The proposed two TNTC approaches are based on the transmission switching distribution factor (TSDF) and flow transfer distribution factor (FTDF), respectively. FTDF‐based TNTC approach is an enhanced version of the TSDF‐based TNTC approach by considering network flow distribution. Numerical simulations demonstrate that both methods can effectively relieve flow violations and FTDF outperforms TSDF.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".