Using a Differential Magnetometer Technique to Measure Geomagnetically Induced Currents: An Augmented Approach
Bibliographic record
Abstract
Abstract Geoelectric fields produced by time‐varying magnetic fields during geomagnetic storms can result in potentially damaging geomagnetically induced currents (GICs) in long conductors at the Earth's surface. GICs can pose a significant risk to the integrity of grounded electrical infrastructure, particularly high‐voltage transformers. In this study, an inferred GIC is calculated using an augmented differential magnetometer measurement (DMM) technique on a 500 kV transmission line in central Alberta and is validated using a proximal transformer neutral‐to‐ground (TNG) current measurement by AltaLink L.P. using a Hall probe at a transformer substation. This research outlines a custom‐built and innovative DMM design by which both DMM sensors deployed around a power line measure the background geomagnetic disturbance (GMD) field and the magnetic field generated locally by the GIC. We show how this modified approach provides two independent estimates for GIC derived using only ΔBy or ΔBz, the magnetic field components perpendicular to the line carrying GIC. Results for a geomagnetic storm on 12 Oct 2021 show contemporaneous peaks in the TNG current and the DMM‐inferred GIC. The two data sets have similar waveforms and are within the same order of magnitude. The background GMD is reconstructed using DMM and shows excellent correlation to the measured GMD at the permanent Canadian Array for Real‐time Investigations of Magnetic Activity magnetic station at Ministik Lake, approximately 48.5 km away. Based on the results presented here, we verify the added utility value of DMM for temporary deployments for assessing GIC risk in electrical power grids.
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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 | 0.001 |
| 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".