3-D FDTD Analysis of Lightning-Induced Voltages in Distribution Lines Due to Inclined Lightning
Bibliographic record
Abstract
In this article, lightning-induced overvoltages due to inclined lightning are investigated by a finite-difference timedomain (FDTD) method for distribution lines of a single conductor and a multiphase line (three-phase conductors with a shield wire, utility poles, groundings, and arresters). The FDTD model is validated in comparison with results of circuit-theory-based analytical and numerical calculation methods. In the single conductor line, the influences of inclined lightning on the induced voltage waveform, peak voltage, and voltage profile along the line are investigated with several combinations of lightning distances, earth resistivities, and lightning current waveforms. The induced voltage is significantly influenced by the lightning inclination toward the line (angle θ). The increased ratio of the peak voltage by the angle θ becomes larger with lower earth resistivity and shorter rise time of the lightning current. While the inclination along the line (angle φ) makes only minor differences on the peak voltage, the voltage profile along the line becomes asymmetric. In the multiphase line, the influence of the angle θ on the voltage becomes similar to that in the single conductor line. These results clearly indicate that the lightning inclination should be considered for an accurate evaluation of lightning-induced voltages.
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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 | 0.000 | 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".