Presentation of an approach based on EMTP for the calcula- tion of lightning induced overvoltages
Bibliographic record
Abstract
LIPS (Lightning Impact on Power Systems) is a toolbox based on EMTP (DCG version) and devoted to the calculation of the failure rate of apparatus due to lightning. This software covers direct and induced lightning. It has been developed in a partnership project between France Telecom, Hydro-Quebec and Electricite de France. The induced light- ning module calculates overvoltages due to a lightning flash nearby the electrical system. It is based on the following com- putational steps: 1) For each overhead line and underground cable of the electrical system, two equivalent current sources are cal- culated in the frequency domain, representing the illumi- nation of the conductors by external electrical field due to lightning. These sources are evaluated in the frequency domain, taking into account the variation of the parame- ters of lines and cables with frequency. The conductivity of the ground is taken as finite. The modal theory is ap- plied in the case of multi-conductor cable or line. 2) The equivalent sources are converted into time-domain and then included in an EMTP representation of the system allowing a time-domain transient computation. After providing theoretical background this paper will pre- sent this approach and follow by comparisons between in- duced overvoltages calculated by LIPS and results from the literature.
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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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
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".