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Record W4388653502 · doi:10.1080/08123985.2023.2281617

AusLAMP shines a light on space weather hazards in the Australian high-voltage power grid

2023· article· en· W4388653502 on OpenAlexaboutno aff
Liejun Wang, Jingming Duan, Adrian Hitchman, Matthew Gard, R. A. Marshall, Andrew Lewis, William V. Jones

Bibliographic record

VenueExploration Geophysics · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicEarthquake Detection and Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsSpace weatherGeomagnetic stormGeomagnetically induced currentEarth's magnetic fieldMagnetotelluricsElectric power transmissionStormMeteorologyElectric power systemGeophysicsPower transmissionGeologyClimatologyEnvironmental sciencePower (physics)Magnetic fieldElectrical engineeringElectrical resistivity and conductivityPhysicsEngineering

Abstract

fetched live from OpenAlex

A geomagnetic storm, also known as a geomagnetic disturbance (GMD), is a major disturbance of the Earth’s magnetic field caused by solar activity. A geomagnetic storm induces electric currents in the Earth that feed into power lines through substation neutral earthing, causing instabilities and even blackouts in electricity transmission systems. The intensity of geomagnetically induced currents (GICs) is closely associated with the electrical conductivity of the surrounding geology. In this paper, we analyse one of the most well-known geomagnetic storms, the 1989 “Québec storm” and 688 magnetotelluric (MT) survey sites from the Australian Lithospheric Architecture Magnetotelluric Project (AusLAMP) to gain insight into the space weather hazard posed for Australia's modern-day power grids. Transmission lines may exhibit local maxima at differing times depending on their spatial orientation and length with respect to the time-varying magnetic field. Localised peak voltages over 100 V can be observed on some individual lines. This assessment identifies the distribution of GICs in south-eastern Australia for the 1989 Québec storm and transmission lines that are more vulnerable to GICs. It is relevant to national strategies and risk assessment procedures to mitigate space weather hazards in the Australian high-voltage power grid and the design of a more resilient power transmission system. We also analyse the 2015 “St Patrick’s Day storm” to study under-estimation of the space weather hazard associated with the band-limited geomagnetic data and MT data sets.Key points The subsurface geology has a great influence on the intensity of geomagnetically induced electric fields, potentially causing up to three orders of magnitude difference between conductive basins and resistive cratonic regions in south-eastern Australia.Analysis using the 1989 “Québec geomagnetic storm” and AusLAMP magnetotelluric data shows the intensity of the geoelectric fields in south-eastern Australia could reach up to 5 [V/km].Geomagnetically induced voltages in the Australian high-voltage power grid could be in excess of 100 V in some transmission lines for a geomagnetic storm with intensity comparable with the 1989 Québec geomagnetic storm.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.229
Teacher spread0.211 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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