Simulation of Geomagnetically Induced Currents in a Low‐Latitude 500 kV Power Network During a Solar Superstorm
Bibliographic record
Abstract
Abstract Geomagnetically induced currents (GICs) are one of the main manifestations through which space weather affects human technical facilities, and GICs constitute the final link in the solar wind‐magnetosphere‐ionosphere‐ground interaction chain. Therefore, understanding the responses of power grids to solar superstorms is crucial for space weather research and emergency preparedness purposes. In this work, we combine the Space Weather Modeling Framework with a ground conductivity model and a model of the Chinese Guangdong 500 kV power grid to investigate the response of the whole power grid if the 23 July 2012 solar superstorm had struck the Earth. The maximum |GICs| produced in the power grid by this event reach approximately 400 A, which is more than thrice those measured during a strong magnetic storm with Kp = 8. Based on statistical analyses of the strength and duration of large GICs at 54 substations and a comparison with the GICs responsible for the Hydro‐Québec power outage on 13 March 1989, we stipulate that the risk of GICs in the Guangdong 500 kV power grid is very high. The simulation results also reveal that field‐aligned currents can play an important role in producing GICs in middle‐ and low‐latitude power grids during solar superstorms. This finding provides crucial insight for understanding the factors that generate strong GICs at middle and low latitudes.
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 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".