Comparison of methods for modelling geomagnetically induced currents
Bibliographic record
Abstract
Abstract. Assessing the geomagnetic hazard to power systems requires reliable modelling of the geomagnetically induced currents (GIC) produced in the power network. This paper compares the Nodal Admittance Matrix method with the Lehtinen–Pirjola method and shows them to be mathematically equivalent. GIC calculation using the Nodal Admittance Matrix method involves three steps: (1) using the voltage sources in the lines representing the induced geoelectric field to calculate equivalent current sources and summing these to obtain the nodal current sources, (2) performing the inversion of the admittance matrix and multiplying by the nodal current sources to obtain the nodal voltages, (3) using the nodal voltages to determine the currents in the lines and in the ground connections. In the Lehtinen–Pirjola method, steps 2 and 3 of the Nodal Admittance Matrix calculation are combined into one matrix expression. This involves inversion of a more complicated matrix but yields the currents to ground directly from the nodal current sources. To calculate GIC in multiple voltage levels of a power system, it is necessary to model the connections between voltage levels, not just the transmission lines and ground connections considered in traditional GIC modelling. Where GIC flow to ground through both the high-voltage and low-voltage windings of a transformer, they share a common path through the substation grounding resistance. This has been modelled previously by including non-zero, off-diagonal elements in the earthing impedance matrix of the Lehtinen–Pirjola method. However, this situation is more easily handled in both the Nodal Admittance Matrix method and the Lehtinen–Pirjola method by introducing a node at the neutral point.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".