ACCEPTED BY IEEE TRANSACTIONS ON POWER SYSTEMS 1
Bibliographic record
Abstract
Abstract—Many blackouts in electric power grids throughout the world are caused by cascading outages, which often involve complex processes in various timescales. The multi-timescale nature of cascading outages makes conventional quasi-static simulation methods inaccurate in characterizing actual evolution of outages. This paper proposes a multi-timescale cascading outage model using a quasi-dynamic simulation method. The model establishes a framework for simulating interactions among dynamics in quite different timescales. It realizes simulation of cascading outages with representation of time evolution, so it overcomes ambiguity of time in conventional cascading outage models and hence has better practicality. Moreover, the model considers dynamics, e.g. load variation and generator excitation protection which affect voltage and reactive power profiles. Also, an improved re-dispatch model based on sensitivity is proposed. These improvements facilitate better simulation for a realistic power system. Also, dynamic simulation can be flexibly incorporated into the simulation of short-term processes in this model as needed. Case studies with the proposed multi-timescale model on the IEEE 30-bus system discuss the role of generator protection in cascading outage evolution, and analyze stage characteristics in outages. The multi-timescale model is also demonstrated on a reduced 410-bus US-Canada northeast power grid. Moreover, impacts from dispatchers ’ involvements are analyzed. Index Terms — cascading outage, time evolution, reactive power, multi-timescale, quasi-dynamic I.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.245 | 0.093 |
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".