MétaCan
Menu
Back to cohort

AC Electromagnetic Interference Study between Railways and Nearby Power Lines under Steady-State Operation

2023· article· en· W4379524577 on OpenAlexaff
Chenyang Wang, Xiaodong Liang, Emerson Adajar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Systems and Energy Efficiency
Canadian institutionsUniversity of SaskatchewanManitoba Hydro
Fundersnot available
KeywordsEMIElectromagnetic interferenceBallastElectrical engineeringEngineeringLine (geometry)Power (physics)Electric power transmissionVoltageElectric power systemSensitivity (control systems)Automotive engineeringElectronic engineeringPhysics

Abstract

fetched live from OpenAlex

AC electromagnetic interference (EMI) between railways and nearby power lines can compromise reliable operations of railway signal and protection systems and increase electrical shock hazards to the public and railway personnel. To maintain the continuity of railway operation and meet the railway safety standard, an AC EMI study is crucial. However, modeling AC EMI patterns in railways due to nearby power lines is complex, involving parameterization and characterization of AC EMI, and modeling the overall railway-power line system. Variations of design parameters along the right-of-way of rail tracks should also be considered. In this paper, the steps to conduct an AC EMI study for a rail system in the vicinity of a power line using the CDEGS software package is proposed. Sensitivity studies are conducted considering variations of several parameters, including the separation distance between railways and nearby power lines, length of the railway, ballast resistance, soil resistivity, power line loading, and voltage class of power lines. Based on results of these studies, a general guideline is developed in this paper to recommend the minimum separation distances between railways and power lines to mitigate AC EMI. An initial AC EMI assessment for a particular railway-power line system can be conducted using this guideline to determine if there are safety concerns and a detailed and costly engineering study is required.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.537
Threshold uncertainty score0.520

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 head0.016
GPT teacher head0.234
Teacher spread0.218 · 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 designSimulation or modeling
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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same topicRailway Systems and Energy EfficiencyFrench-language works237,207