Time-varying contingency screening for dynamic security assessment using intelligent-systems techniques
Bibliographic record
Abstract
A time-frequency-based approach for contingency severity ranking and rapid stability assessment is described. The aim is to classify correctly all single or multiple contingencies that may result in loss of voltage or frequency stability in the first 20 s following the last disturbing action. We start by selecting a number of strategic monitoring buses where the phasor measurement units are located to capture representative voltage magnitudes and angles during detailed time-domain simulations, which cover special protection systems and on-load tap-changers. The short-time Fourier transform is then dynamically applied to the responses for extracting selected decision features as the simulation time evolves. It is shown that frequency-domain features such as the peak spectral density of the angle, frequency and their dot product evaluated over the grid areas are reliable time-varying stability indicators that can form the basis of an entirely secure classification system able to respond within 2 to 3 s after the last event in the contingency. This allows early termination of about 60% of permanently stable simulations. Fuzzy logic and neural networks are used together to make initial decisions which are then mixed by voting in order to improve the assessment reliability and security at the expense of a reduced yield. The proposed DSA scheme is successfully tested with 1027 contingencies from two widely differing test systems: a 67-bus fictitious system and a 783-bus system in actual use at Hydro-Quebec's operations planning department.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".