On the Role of Shield Wires in Mitigating Lightning-Induced Overvoltages in Overhead Lines - Part I: A Critical Review and a New Analysis
Bibliographic record
Abstract
The ability of shield wires installed in overhead lines to mitigate lightning-induced overvoltages has been extensively investigated. Unfortunately, these studies came to different results, sometimes contradicting each other: some authors found that shield wires produce a significant overvoltage reduction, while others found the reduction negligible; conflicting results also pertain to the role played by the various parameters involved, such as the relative height of the shield wire(s) compared to the phase conductors. This paper aims to clarify this topic. The paper is organized in two parts: Part I, which starts from the analysis of the theory behind the mitigation effect, is devoted to establishing a more solid base to the topic. Two fundamental improvements are proposed. The first one is the distinction betweeninternalandexternalof the parameters involved: current literature makes an indiscriminate grouping of all of them; the second one is concerned with the point along the line where the mitigation effect needs to be assessed. Thanks to this new approach, we show that this effect can be precisely quantified. The analysis in this Part I is limited to the basic case of a single grounding point of the shield wire, which represents an unrealistic case. Part II is devoted to completing the study, by applying the proposed approach to more realistic and practical cases.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".