Contingency analysis via SDP relaxations of the OPF problem
Bibliographic record
Abstract
We aim to solve the OPF problem by defining contingency scenarios involved in the optimization problem by means of a SDP relaxation. Contingency scenarios are performed for the IEEE 39-bus system. Contingency analyses are performed by generation and line losses. Contingency scenarios are considered to be possible events. These events may be single contingencies, such as a generation unit failures or transmission line failures, or they may be double contingencies. Transmission line contingencies occur with the loss of critical lines of the transmission grid on the system. After single or multiple contingencies occur, it is required to shed some load, drop or trip the generation, or trip transmission lines for maintaining secure grid operations. In our contingency scenarios, we basically determine the appropriate amount of load to be shed. The SDP based OPF problem is tested for feasible transactions and generation dispatches in the system. if such action is feasible, the global optimal solution of the SDP-based OPF problem is guaranteed. In addition, the impact of worst-case contingency is examined for our test system. The amount of load shedding required is thus obtained.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".