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Record W2094735185 · doi:10.1002/etep.4450130205

An economic parallel processing technology for faster than real‐time transient stability simulation

2003· article· en· W2094735185 on OpenAlexaff
S. Beaudin, R.J. Marceau, Gérard Bois, Yvon Savaria, N. Kandil

Bibliographic record

VenueEuropean Transactions on Electrical Power · 2003
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversité de MontréalUniversité de SherbrookePolytechnique MontréalNatural Sciences and Engineering Research Council of Canada
Fundersnot available
KeywordsTransient (computer programming)Electric power systemStability (learning theory)Computer sciencePower (physics)EngineeringElectronic engineering

Abstract

fetched live from OpenAlex

Abstract The need to increase power flows is forcing electric power companies to consider the rapid determination of transfer and security limits in energy management centers. At the present time, transient stability transfer limits are generally calculated off‐line due to the limitations of numerical computers. This paper introduces an innovative approach for simulating transient stability based on the use of a new type of chip technology designed for real‐time image processing. Power system equations in matrix form are solved by means of rapid matrix operation techniques on the PULSE (parallel ultra large scale engine) technology, resulting in outstanding low cost performance. This paper confirms the efficiency of this approach on the 9 bus Western Systems Coordinating Council (WSCC) test power system. Simulation results based on a preliminary version of the PULSE technology clearly indicate that economic, faster than real‐time transient stability simulation is feasible for large networks.

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.

How this classification was reachedexpand

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.960
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.011
GPT teacher head0.231
Teacher spread0.220 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2003
Admission routes1
Has abstractyes

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