Analysis of Contingencies Leading to Islanding and Cascading Outages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We develop and test an efficient simulator to estimate the sequence of automatic events that follow a contingency leading to islanding and cascading outages. The simulator is based on a quasi-steady-state model o that includes island identification, island power balance, primary frequency regula tion, under-frequency load shedding, over-frequency generator tripping, and island load flow. The simulator first identifies the islands into which the network splits through the null space of the network susceptance matrix. Each island is then analyzed for a surplus or deficit of primary frequency regulation, followed by any necessary corrective load shedding or generation tripping. Next, each island's load flow is solved and tested for possible new line overloads and corresponding line tripping. The overall contingency analysis reruns until either all loads are shed or the islands can operate at some reduced load level within their operational bounds. Simulation results are presented for a 10- bus, 13-line system and for the IEEE 73-bus, 119 line reliability test network.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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