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Record W2102954307 · doi:10.1109/59.871738

Contingency ranking for on-line voltage stability assessment

2000· article· en· W2102954307 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Power Systems · 2000
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsElectric power systemContingencyReliability engineeringRanking (information retrieval)Margin (machine learning)Stability (learning theory)EngineeringVoltageBlackoutPower (physics)Computer scienceRank (graph theory)Line (geometry)Control theory (sociology)Control (management)Electrical engineeringMathematicsMachine learningArtificial intelligence

Abstract

fetched live from OpenAlex

Voltage stability has become a challenging problem for many power systems. Contingency screening and ranking (CS&R) is one of the important components of on-line voltage stability assessment. The objective of CS&R is to quickly and accurately select a short list of critical contingencies from a large list of potential contingencies and rank them according to their severity. Suitable preventive control actions can be implemented considering contingencies that are likely to affect the power system performance. An effective method for contingency ranking is proposed in this paper. This method calculates the voltage stability margin considering branch outages. Test results based on the IEEE 300 bus power system, a 600 bus power system as well as two utility systems (197 bus and 1254 bus) are presented.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.982
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.025
GPT teacher head0.262
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it