Real‐time detection of vulnerable power system areas to geomagnetic disturbance
Bibliographic record
Abstract
This study proposes an approach for the fast and accurate identification of the vulnerable areas of the power system to geomagnetic disturbance (GMD). The proposed method can be used for real‐time situational awareness and preparedness in power system control rooms and proactive mitigation of the GMD threats. In addition, it can be employed as an off‐line GMD vulnerability assessment tool for power system planning analysis. In this study, a generalised mathematical basis is presented to find the maximum geomagnetically induced current (GIC) flow in the multi‐zone earth structure based on the orthogonal GIC components. Furthermore, a real‐time frequency estimation method is developed based on wavelet transform to estimate the frequency of the geomagnetic waveform for the maximum GIC calculation. The proposed approach is applied to the Ontario 500 and 230 kV transmission systems to identify the vulnerable equipment. The results are also compared with the angle sweep results. The numerical results reveal that not only the proposed method is more accurate but also significantly faster than the angle sweep method. Such prominent features introduce the proposed approach as a preferable method for real‐time applications and optimisation of power system operation during GMDs.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".