Slow-Coherency-Based Controlled Islanding—A Demonstration of the Approach on the August 14, 2003 Blackout Scenario
Bibliographic record
Abstract
This paper demonstrates the use of a slow-coherency-based generator grouping algorithm and a graph theoretic approach to form controlled islands as a last resort to prevent cascading outages following large disturbances. The proposed technique is applied to a 30 000-bus, 5000-generator, 2004 summer peak load, Eastern Interconnection data and demonstrated on the August 14, 2003 blackout scenario. Adaptive rate of frequency decline-based load shedding schemes are used in the load rich islands to control frequency. The simulation results presented show the advantage of the proposed method in containing the impact of the disturbance within the islands formed and in preventing the impact of the disturbance from propagating to the rest of the system. This is demonstrated by the significant reduction in line flows in the rest of the system and by improved voltage and relative angle characteristics. Based on the suggestion in the joint U.S.-Canadian task force final report on the blackout, load shedding without any islanding is also performed, and results obtained are compared with the proposed controlled islanding method. The islanding method outperforms the load shedding-only method in reducing the transmission line flows, but both methods have similar effects on voltage and relative angle behavior
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".