Transformer‐Level Modeling of Geomagnetically Induced Currents in New Zealand's South Island
Bibliographic record
Abstract
Abstract During space weather events, geomagnetically induced currents (GICs) can be induced in high‐voltage transmission networks, damaging individual transformers within substations. A common approach to modeling a transmission network has been to assume that every substation can be represented by a single resistance to Earth. We have extended that model by building a transformer‐level network representation of New Zealand's South Island transmission network. We represent every transformer winding at each earthed substation in the network by its known direct current resistance. Using this network representation significantly changes the GIC hazard assessment, compared to assessments based on the earlier assumption. Further, we have calculated the GIC flowing through a single phase of every individual transformer winding in the network. These transformer‐level GIC calculations show variation in GICs between transformers within a substation due to transformer characteristics and connections. The transformer‐level GIC calculations alter the hazard assessment by up to an order of magnitude in some places. In most cases the calculated GIC variations match measured variations in GIC flowing through the same transformers. This comparison with an extensive set of observations demonstrates the importance of transformer‐level GIC calculations in models used for hazard assessment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".