On the flow of geomagnetically induced currents in an electric power transmission network
Bibliographic record
Abstract
Geomagnetically induced currents (GICs) in conductor networks are among the ground-level effects of space weather. GICs are a possible source of problems to the system. Today, electric power transmission grids are the most important concern regarding GICs, which may in the worst cases lead to blackouts in large areas and permanent damage to transformers. The evaluation of GIC risks and the design of possible countermeasures require estimation of expected GIC magnitudes in transformers. This can be achieved by model calculations supplemented by GIC recordings at some sites. Although in principle GICs can flow all over a large galvanically-connected power grid, which should thus be included as a whole in a GIC calculation, the network must usually be restricted somehow in practical computations of GICs. By using a power grid test model, this paper provides a systematic numerical investigation showing that GICs do not flow over very long distances in a power grid, which is a good result and justifies the neglect of the parts of the network that lie far away from the area of primary interest. Besides practical significance in electric power engineering, studies of GICs can be used for space physics and geophysical research as well. It is also important to understand the features of the flow pattern of GICs in a network.
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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".