The Influence of 3‐D Earth Conductivity, Geoelectric Field Polarization, and Power Grid Topology on GIC Risk
Bibliographic record
Abstract
Abstract Geomagnetically induced currents (GICs) are naturally occurring electrical currents that flow through the Earth and long conductive infrastructure, such as power lines, during geomagnetic storms. GICs in the electric power grid can cause damage to electric power transformers, system failures, and wide‐scale blackouts. Here, the combined effects of the power network's topology and the Earth's underlying conductivity structure on GIC risk are examined using examples from the electric power grid in Alberta, Canada. Our results show that the electric field polarization generated by geomagnetic storms is strongly dependent on the subsurface conductivity structure. Moreover, due to Earth induction effects, the two components of the transverse electric fields can also be highly correlated in specific geological regions. Combined, this creates a preferred electric field directionality, presenting a GIC risk for power grids with specific directional topology. A direct comparison between the geoelectric field and the measured transformer neutral‐to‐ground (TNG) currents measured near Edmonton, Alberta, on 24‐04‐2023 is shown. At this location, the two horizontal components of the geoelectric field are strongly correlated, and and show strong temporal and waveform correspondence with the TNG current. Two truth tables illustrate the increased or decreased GIC risk in such cases demonstrating that the GIC in a segment of the electric power network may combine constructively or deconstructively depending on the power network configuration and the relative orientation of the geoelectric field polarization. This is further exemplified by a case study of two real‐world network configurations in central Alberta.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".