Evaluation of Simulation Methods for Analysis of Geomagnetic Disturbance System Impacts
Bibliographic record
Abstract
Geomagnetic Disturbance (GMD) impacts a power system by causing the circulation of quasi-dc Geomagnetically-induced Currents (GICs) in transmission lines and high-voltage transformer windings leading to transformer saturation, increased reactive power losses, and voltage regulation problems. Utility planners use various analysis methods and simulation tools to analyze GMD system impacts. These techniques can be broadly categorized into load-flow-based (LF), transient stability type (TS), and electromagnetic transient type (EMT) methods. These methods are based on different modeling assumptions and solution techniques. There is a need for evaluation of these assumptions and cross-examination of results to ensure their accuracy. Such an evaluation is essential to ensure that planners performing required GMD vulnerability assessments have confidence in the results. This paper evaluates the LF, TS, and EMT methods through cross-examination of their results. The objective is to identify their limitations, assess the consistency of their results, and provide assumptions on their use for analysis of GMD system impacts. The study is conducted in consistence with the North American Electric Reliability Corporation (NERC) GMD guidelines and standards.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".