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Record W2131151364 · doi:10.1109/tpwrs.2002.807050

Effect of detailed power system models in traditional and voltage-stability-constrained optimal power-flow problems

2003· article· en· W2131151364 on OpenAlexaff
William Rosehart, C.A. Caizares, V.H. Quintana

Bibliographic record

VenueIEEE Transactions on Power Systems · 2003
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of WaterlooUniversity of Calgary
Fundersnot available
KeywordsControl theory (sociology)AC powerElectric power systemGenerator (circuit theory)Power flowVoltageVoltage optimisationStability (learning theory)Mathematical optimizationPower-flow studyStatic VAR compensatorVolt-ampere reactivePower (physics)Computer scienceEngineeringMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

In this paper, detailed generator, exponential load, and static volt-ampere-reactive (VAr) compensator models are incorporated into traditional and voltage-stability-constrained optimal power-flow problems to study the effect that the different models have on costs and system-loadability. The proposed models are compared to typical models by means of a detailed analysis of the results obtained for two IEEE test systems; interior point methods are used to obtain numerical solutions of the associated optimization problems.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.009
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.196
Teacher spread0.183 · 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 source (direct Gemma or distilled Codex), 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

Citations64
Published2003
Admission routes1
Has abstractyes

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