AC Mitigation Design Considerations for Pipeline Facilities
Bibliographic record
Abstract
Abstract AC interference between co-located pipelines and high voltage AC powerlines can result in safety hazards to operating personnel and the public under powerline steady-state and fault conditions. Under steady-state conditions, AC voltages are induced on the pipeline via electromagnetic coupling. Under fault conditions, high AC voltages can be present via electromagnetic coupling and AC currents discharging into the earth from the powerline structures, i.e., conductive coupling. These high voltages can result in serious injury or death to any person in contact with any above-ground metallic appurtenance that is electrically continuous with the pipe. The type and magnitude of AC interference hazards are dependent on many factors including the co-location configuration, the electrical resistivity of the soil, the AC currents on the powerline, and the powerline and pipeline characteristics. Consequently, AC mitigation strategies must be put in place to ensure the safety of all persons and must be tailored according to the various conditions. This paper discusses the factors that should be considered when designing AC mitigation systems for pipeline facilities. Such factors include facility layout, isolation points, fencing, and ground conditions. The paper also discusses the different mitigation strategies that can be implemented including gradient control grids, ensuring electrical continuity, and grounding.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".