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Record W2959996238 · doi:10.5006/c2018-11231

Effects of Seasonal Variation on AC Interference and Mitigation Design

2018· article· en· W2959996238 on OpenAlexaffabout
Wolfgang Fieltsch, Ernesto Gudino, Travis Wymenga

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Line Communications and Noise
Canadian institutionsTransCanada (Canada)Stantec (Canada)Cochrane
Fundersnot available
KeywordsInterference (communication)SeasonalityEnvironmental scienceVariation (astronomy)Electromagnetic interferenceAutomotive engineeringComputer scienceElectrical engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract In Canada and in the northern portions of the United States, soil resistivities increase rapidly by up to several orders of magnitude once water in the soil freezes in the winter. Frost-penetration can vary from centimeters up to a meter or more in depth. Although it is standard industry practice in the electrical industry to consider seasonal variations in soil resistivity when modeling grounding facilities for substations and generating stations, this is often not considered in AC interference studies related to pipelines. The effect on AC induction is expected to be minimal, as this is predominantly dependent on the deeper soil layers. However, frozen soil conditions can have a significant impact on the resistive coupling under a phase to ground powerline fault, and the effectiveness of the AC mitigation system. This paper involved a case study of a project involving AC interference on pipelines located in Canada. summer, and winter soil conditions were modeled to determine the effect on the subject pipeline under steady state and fault conditions. Allowable safe touch potentials were also calculated for these two scenarios, and used to assess the safety risks. A mitigation system was designed to ensure that the safety and integrity risks are mitigated to acceptable limits, for both seasonal conditions.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.491
Threshold uncertainty score0.128

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.011
GPT teacher head0.224
Teacher spread0.213 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2018
Admission routes2
Has abstractyes

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