State extension for adequacy evaluation of composite power systems-applications
Why this work is in the frame
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Bibliographic record
Abstract
It is not feasible or even possible to investigate all the possible system states of a large practical composite generation and transmission system, as the number of the system states can be extremely large. The probabilities of the normally uninvestigated high level system outage states are individually very small, but the total value can be significant to the large number of these states. The state extension algorithm can efficiently extend the knowledge of the investigated system states to collectively include the effects of a large number of the uninvestigated system states. The accuracy of the adequacy indices, when using the state extension technique, is therefore improved without investigating the high level system states individually, which requires very large computation times. This paper illustrates the effectiveness of the state extension algorithm by application to two reliability test systems, the RBTS and the IEEE-RTS.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it