Relative Risk of Alternating Current Power Line Faults Affecting Nearby Pipelines
Bibliographic record
Abstract
Abstract The co-location of pipelines and alternating current (AC) transmission lines can lead to electrical hazards on a pipeline. One example is a transient voltage that electrifies a nearby pipeline during a power line to ground fault. A power line fault, typically caused by a lightning strike, cut power line, or windstorm, results in electrical potential being transferred to ground. A buried pipeline near a fault acts as a grounding conductor, carrying energy to an area of lower potential than the incident location. Current carried through a pipe is a hazard to people and equipment. A current travelling to an above ground structure could lead to an individual becoming part of an electrical circuit if they touch the structure and the possibility of a high electrical current flowing through an individual’s entire body. While current literature describes how to mitigate the effects of power line faults, there are limited sources that describe a process to quantify the probability of power line faults affecting pipelines. In this paper, a method to assess the frequency of AC powerline faults and their potential impact to pipeline infrastructure is described. The model incorporates spatial and historical factors to evaluate the exposure of individual assets to AC power line faults. It estimates the powerline fault frequency and the area of ground potential rise that could lead to safety consequences for workers or members of the public. The collection of fault frequency data, the calculation of fault current from transient voltage hazards, and the estimated area of potential harm from transient voltage hazards are discussed. The model was developed to rank the risk of power line fault incidents across a company’s pipeline system. The results of the assessment help prioritize locations to perform more detailed site-specific analysis for the design and installation of mitigation systems.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".