MétaCan
Menu
Back to cohort

Comparison of Harmonic Blocking Methods in Transformer Differential Protection under GIC Conditions

2021· article· en· W3216574805 on OpenAlexaff
Babak Ahmadzadeh‐Shooshtari, Afshin Rezaei‐Zare

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsYork University
Fundersnot available
KeywordsGeomagnetically induced currentDifferential protectionDelta-wye transformerEnergy efficient transformerTransformerLinear variable differential transformerIsolation transformerRotary variable differential transformerCurrent transformerDistribution transformerTransformer effectEngineeringElectrical engineeringElectronic engineeringVoltagePhysicsMagnetic fieldGeomagnetic storm

Abstract

fetched live from OpenAlex

Geomagnetically induced current (GIC) causes the transformer core saturation, which increases the magnitudes of harmonic components in the transformer currents. In such a condition, the transformer differential protection can fail to trip and clear internal short-circuit faults, due to harmonic blocking (HB). This paper compares the performance of existing HB methods, employed in the transformer differential relays, during the occurrence of internal faults and under the GIC flow in the transformer. The time-domain simulations, conducted for bolted and unbolted faults under loaded and unloaded transformer conditions, show that there are GIC-based scenarios in which common and independent HB methods cause the differential protection failure to trip the transformer.

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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.054
GPT teacher head0.376
Teacher spread0.322 · 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

Citations5
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicPower Systems Fault DetectionFrench-language works237,207