Full-Wave 3-D Transient Analysis With Method of Moments and Numerical Laplace Transform Including Resistive Non-Linear Elements
Bibliographic record
Abstract
Methods based on both time (t)-domain and frequency (f)-domain have been used for electromagnetic transient (EMT) analysis using both circuit-theory and field-theory approaches. Methods based on circuit theory are fast and convenient but may become inapplicable to many engineering problems due to simplifications such as the transverse electromagnetic assumptions. Moreover,t-domain-based techniques rely on approximate methods to account for frequency dependence, which is an essential characteristic of electric conductors, equipment and soil environment. In this paper,f-domain solutions obtained from the method of moments (MoM) discretization of the electric field integral equation (EFIE) are converted tot-domain using the numerical Laplace transform (NLT) by means of post-processing. This circumvents the need to formulate the EFIE and MoM based on the complex frequency which would be required for their application to conventional NLT. Hence, existing MoM implementations can be used to perform a full-wave 3-D EMT analysis for a wide range of power system applications. Examples include energizations of 3-D power system networks with fast and non-vanishing excitations such as step functions, as well as modeling non-linear elements using the piecewise linear approximation. Results from experimental measurements, finite-differencet-domain method, and EMT-type software confirm the validity of the proposed method for power system transients in the range of microseconds down to nanoseconds.
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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) | 0.001 | 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 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".