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Record W2133395398 · doi:10.1109/ccece.1993.332446

Computer simulation study of voltage stability of a power system

2002· article· en· W2133395398 on OpenAlexaff
Yao-nan Yu, Wenjie Zhang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsJacobian matrix and determinantVariable (mathematics)Electric power systemStability (learning theory)Computer scienceVoltagePower (physics)Control theory (sociology)Matrix (chemical analysis)AC powerInverseControl engineeringMathematicsEngineeringElectrical engineeringApplied mathematicsArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

This paper presents the methods and results of a simulation study of voltage stability of a power system. Important load and reactive power components are included. A nine-machine electric power system is chosen as the working example so that the dynamic interactions among machines in a system are adequately represented. Since it is a very high-order system with many nonlinearities and variable constraints and including both fast transients and slow dynamics, a variable step size is chosen for the integration and a new method is developed to solve the Jacobian matrix equations in each integration step without taking the inverse of a very large matrix directly. Case study results show that all voltage-dependent load and reactive power components are important and must be adequately modeled in voltage stability studies.>

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)0.000
metaresearch head score (Gemma)0.002
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 score0.010
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.215
Teacher spread0.194 · 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

Citations3
Published2002
Admission routes1
Has abstractyes

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