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Record W2409518458 · doi:10.1109/tpwrd.2016.2576400

On the Interaction of Power Transformers and Geomagnetically Induced Currents

2016· article· en· W2409518458 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsNatural Resources Canada
FundersNatural Resources Canada
KeywordsGeomagnetically induced currentInductanceTransformerCurrent transformerElectrical engineeringElectric power systemEngineeringElectronic engineeringVoltageEarth's magnetic fieldPhysicsGeomagnetic stormMagnetic fieldPower (physics)

Abstract

fetched live from OpenAlex

Geomagnetically induced currents (GIC) cause partial saturation of transformers leading to a variety of power system effects. Extensive work has been done to model the transformer response to GIC; however, little consideration has been given to how transformer inductance affects the growth of GIC. Here, we present a new method based on calculating the effective inductance of the transformer experienced by GIC. A step-by-step method is used to calculate each step in the growth of GIC, the fraction of time in saturation, and the effective inductance. The new value of effective inductance is then used to calculate the next step in GIC. Model results for a dc step voltage are in agreement with previously published detailed modelling and show the nonlinear response of transformers. The new technique provides a versatile and fast method for modelling the transformer interaction with GIC and provides a way of assessing the GIC frequencies that need to be considered in assessment of the geomagnetic hazard to a power system.

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.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.975
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.000

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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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