MétaCan
Menu
Back to cohort
Record W4412375718 · doi:10.1109/tia.2025.3587183

Effects of the Loading Level on the Harmonic Distortion Caused by GIC Flows Through $3\phi$ Auto-Transformers

2025· article· en· W4412375718 on OpenAlexafffund
S. A. Saleh, E. W. Zundel, Julian Meng, G. Young-Morris, E. F .S. Hill, S. Brown

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2025
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsEnergie NB Power (Canada)University of New Brunswick
FundersAtlantic Canada Opportunities Agency
KeywordsTotal harmonic distortionTransformerDistortion (music)Power system harmonicsHarmonic analysisElectrical engineeringElectronic engineeringEngineeringPhysicsMaterials scienceVoltage

Abstract

fetched live from OpenAlex

Geomagnetically induced currents (GICs) are quasidc currents that are formed due to geo-magnetic disturbance events. Such quasi-dc currents flow from the ground to power systems, and complete their path through grounded equipment, including 3ϕ auto-transformers. The flow of a GIC through a 3ϕ auto-transformer can lead to a significant harmonic distortion, a large increase in reactive power demands, and/or a large increase copper losses. The severity of GIC flow impacts on a 3ϕ auto-transformer can be dependent on the core design, winding configuration, grounding circuit, and GIC current. This paper discusses possible contributions of loading levels to GIC impacts on a 3ϕ auto-transformer. These possible contributions are analyzed through experimental tests carried out using a laboratory 3ϕ, multi-core auto-transformer. Experimental tests are conducted for various GIC flows, where the loading level is varied. Test results conclude that the loading level of a 3ϕ auto-transformer has minor effects on the levels of harmonic distortion caused by the flow of a GIC.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.882
Threshold uncertainty score0.712

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.253
Teacher spread0.232 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueIEEE Transactions on Industry ApplicationsSame topicNon-Destructive Testing TechniquesFrench-language works237,207