MétaCan
Menu
Back to cohort
Record W3007472460 · doi:10.1109/tpwrs.2020.2976834

Test Systems for Voltage Stability Studies

2020· article· en· W3007472460 on OpenAlexaff
Thierry Van Cutsem, Mevludin Glavić, William Rosehart, Claudio A. Cañizares, Marios Kanatas, Leonardo Lima, Federico Milano, Lampros Papangelis, Rodrigo A. Ramos, Jhonatan Andrade dos Santos, Behnam Tamimi, Glauco N. Taranto, Costas Vournas

Bibliographic record

VenueIEEE Transactions on Power Systems · 2020
Typearticle
Languageen
FieldEngineering
TopicPower System Optimization and Stability
Canadian institutionsUniversity of WaterlooUniversity of Calgary
Fundersnot available
KeywordsElectric power systemVoltageStability (learning theory)Computer scienceControl theory (sociology)Set (abstract data type)EngineeringVoltage optimisationReliability engineeringControl engineeringTask (project management)Voltage regulationPower (physics)Control (management)Electrical engineeringSystems engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

This paper describes the two test systems for voltage stability studies set up by the IEEE PES Task Force on “Test Systems for Voltage Stability Analysis and Security Assessment” under the auspices of the Power System Stability Subcommittee of the Power System Dynamic Performance Committee. These systems are based on previous test systems, making them more representative of voltage stability constraints. A set of representative results are provided for both systems, with emphasis on dynamic simulation. They illustrate various aspects such as long-term dynamics, voltage security assessment, real-time detection, and corrective control of instabilities. The value for educators, researchers and practitioners are emphasized.

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)0.003
metaresearch head score (Gemma)0.011
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.006

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 head0.049
GPT teacher head0.254
Teacher spread0.205 · 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

Citations90
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Power SystemsSame topicPower System Optimization and StabilityFrench-language works237,207