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Record W2952004445 · doi:10.1109/temc.2019.2920271

A Modified IEEE 118-Bus Test Case for Geomagnetic Disturbance Studies–Part I: Model Data

2019· article· en· W2952004445 on OpenAlexafffund
Aboutaleb Haddadi, Afshin Rezaei‐Zare, L. Gérin-Lajoie, Reza Hassani, Jean Mahseredjian

Bibliographic record

VenueIEEE Transactions on Electromagnetic Compatibility · 2019
Typearticle
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsHydro-QuébecYork UniversityPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTransformerGeomagnetically induced currentHarmonicsTest dataEngineeringSoftwareElectric power systemTime domainCurrent transformerElectronic engineeringBenchmark (surveying)GridComputer scienceVoltageControl theory (sociology)Earth's magnetic fieldElectrical engineeringPower (physics)Geomagnetic stormMathematics

Abstract

fetched live from OpenAlex

Power grid test cases for geomagnetic disturbance (GMD) studies need particular data that are not provided by the typical IEEE transmission benchmarks. This two-part paper proposes a test case that contains required modeling details for time-domain simulation of a GMD within an electromagnetic transient program (EMT-type). Compared to existing load-flow-based GMD test cases, the proposed test case offers advantages, such as accurate representation of nonlinear transformer magnetization and interaction between the dc geomagnetically-induced currents and transformer saturation, additional var consumption, harmonics, and voltage regulation problems. Part I presents the test case system and parameter data, and Part II provides simulation results to enable software-to-software validation (future work) as a first step toward the establishment of the model as an official GMD benchmark.

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 score0.009
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.294
Teacher spread0.245 · 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

Citations25
Published2019
Admission routes2
Has abstractyes

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