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Record W2048905248 · doi:10.1109/tpwrd.2013.2281191

Effects of System Characteristics on Geomagnetically Induced Currents

2013· article· en· W2048905248 on OpenAlexaff
Kuan Zheng, D. H. Boteler, Risto Pirjola, Lianguang Liu, Richard A. Becker, Luis Martí, Stephen Boutilier, Sébastien Guillon

Bibliographic record

VenueIEEE Transactions on Power Delivery · 2013
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysical and Geoelectrical Methods
Canadian institutionsNova Scotia Department of EnergyHydro One (Canada)Hydro-QuébecNatural Resources Canada
Fundersnot available
KeywordsGeomagnetically induced currentElectric power systemTransformerElectric power transmissionTopology (electrical circuits)Electrical conductorEngineeringGroundTransmission lineAmplitudeElectrical engineeringVoltagePhysicsMagnetic fieldPower (physics)Geomagnetic stormEarth's magnetic fieldOptics

Abstract

fetched live from OpenAlex

The geomagnetically induced currents (GICs) produced in power systems during magnetic storms are a function of the electric-field amplitude and direction, and the characteristics of the power system. This paper examines the influence of a number of power system characteristics, which include the resistances and structures of the conductors; the length of the transmission lines; the number, type, and resistances of transformers, the substation grounding resistances, and the topology of the network. It is shown that GIC grows with increasing line length but approaches an asymptotic constant value, and a more relevant parameter than the individual line length is the length of the entire system. This paper also derives the effective GIC for a conventional transformer and an autotransformer, and analyzes the behavior of GIC when the network topology changes illustrated with the GIC-Benchmark Model. The results of these studies provide a guide to estimating GIC impacts on a power network.

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)0.007
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations90
Published2013
Admission routes1
Has abstractyes

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