Wavelet analysis of geomagnetically induced currents during the strong geomagnetic storms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The main problem of electric utilities around the world is to ensure continuous power supply to consumers. One of the causes of power outages and blackouts can be geomagnetic storms during periods of the increased solar activity. They arouse geomagnetically induced currents (GICs) flowing in the long-distance high-voltage power grids on Earth’s surface. The history of this phenomenon investigation shows that GICs during strong geomagnetic storms had led to blackouts in certain regions of Canada, Sweden and the USA. To study these phenomena and assess the risks of such accidents for the regional system, a GICs registration system in 330 kV autotransformers neutrals of the Kola-Karelian power transit was developed in northwestern Russia. During 11 years of monitoring numerous cases of the flow of high values of quasi-dc currents with different time durations, induced by variations of the geomagnetic field, have been registered. In order to analyze the currents a wavelet transform was chosen, since this method allows to define not only the frequency composition but also changes in spectral characteristics over time, which is significant in the study of GIC. The paper presents a discussion of GIC scalograms obtained for four events of Solar Cycle 24: 13-14 November 2012, 17-18 March 2015, 7-8 September 2015 and 7-8 September 2017. The analysis showed that the characteristic duration of the peak of the considered GICs is from 4.6 to 11.1 min.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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 it