Prediction of geomagnetically induced currents (GICs) flowing in Japanese power grid for Carrington-class magnetic storms
Bibliographic record
Abstract
Abstract Large-amplitude geomagnetically induced currents (GICs) are the natural consequences of the solar–terrestrial connection triggered by solar eruptions. The threat of severe damage of power grids due to the GICs is a major concern, in particular, at high latitudes, but is not well understood as for low-latitude power grids. The purpose of this study is to evaluate the lower limit of the GICs that could flow in the Japanese power grid against a Carrington-class severe magnetic storm. On the basis of the geomagnetic disturbances (GMDs) observed at Colaba, India, during the Carrington event in 1859, we calculated the geoelectric disturbances (GEDs) by a convolution theory, and calculated GICs flowing through transformers at 3 substations in the Japanese extra-high-voltage (500-kV) power grid by a linear combination of the GEDs. The estimated GEDs could reach ~ 2.5 V/km at Kakioka, and the GICs could reach, at least, 89 ± 30 A near the storm maximum. These values are several times larger than those estimated for the 13–14 March 1989 storm (in which power blackout occurred in Canada), and the 29–31 October 2003 storm (in which power blackout occurred in Sweden). The GICs estimated here are the lower limits, and there is a probability of stronger GICs at other substations. The method introduced here will be immediately applicable for benchmark evaluation of low-latitude GICs against the Carrington-class magnetic storms if one assumes electrical parameters, such as resistance of transmission lines, with sufficient accuracy.
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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".