MétaCan
Menu
Back to cohort
Record W1984398169 · doi:10.1109/pesmg.2013.6672072

Generator thermal stress during a Geomagnetic Disturbance

2013· article· en· W1984398169 on OpenAlexaff
Afshin Rezaei‐Zare, Luis Martí

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsHydro One (Canada)
Fundersnot available
KeywordsHarmonicsTransformerEmtpGeomagnetically induced currentGenerator (circuit theory)Permanent magnet synchronous generatorControl theory (sociology)VoltageHarmonic analysisMagnetohydrodynamic generatorElectrical engineeringEngineeringComputer sciencePhysicsEarth's magnetic fieldElectric power systemPower (physics)Electronic engineeringGeomagnetic stormMagnetic field

Abstract

fetched live from OpenAlex

This paper investigates the operating condition of the generator during a Geomagnetic Disturbance (GMD). Generators are sensitive to harmonics and negative sequence currents, caused by the half-cycle saturation of the generator step-up transformer due to Geomagnetically Induced Current. Such harmonic currents can cause rotor heating, alarming, and the loss of generation. Based on the time-domain simulation in the EMTP, this study investigates the order and magnitude of the harmonics which impact the generator, and determines the rotor heating level due to such harmonics, at various levels of the GIC. The study reveals that the generator can reach its thermal capability limit at moderate GIC levels. However, the existing standards, e.g., IEEE Standards C50.12 and C50.13, fail to account for such operating conditions, and the corresponding recommendations underestimate the rotor heating level. As such, the negative sequence relays may not accurately operate under GMDs. A modification to the standards is also required which is proposed in this study.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.003
GPT teacher head0.159
Teacher spread0.156 · 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 designObservational
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

Citations18
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicPower Systems Fault DetectionFrench-language works237,207