Bibliographic record
Abstract
Power system security and reliability has a higher priority in power system operations. Power systems are exposed to any failures due to their structures. Preventing any unscheduled outage from happening within the power system is impossible, but analyzing possible outages in order to predict their consequences is essential. Contingency analysis is an important tool in evaluating power system security. It models any single or multiple outages to predict power system state variables after them. By analyzing and preparing for outages, their consequences can be contained. The N-1 contingency which models any single outages of a power system is studied. A DC power flow is used to identify critical single line outages, and the selected critical contingencies are evaluated in detail by an AC power flow. A DC power flow performance in estimating line active power flow is evaluated by an appropriate index. It is shown that a DC power flow has an acceptable performance in contingency analysis. The main goal of this study is to identify critical double line outages whose outage will lead to line flow violations in a power system. This is defined as N-2 contingency analysis. Evaluating all possible N-2 contingencies is a huge burden computationally. Identifying important double line outages without evaluating all N-2 contingencies by either an AC power flow or DC power flow is possible. Screening algorithms are used to identify critical outages based on line outage distribution factors and N-1 contingency analysis. The results are compared to the ones obtained from full AC power flow. It is shown that these algorithms are able to identify a very high percentage of the double line outages that result in line flow violations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".