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Record W2022427525 · doi:10.1109/jpets.2015.2395388

LineScout Technology Opens the Way to Robotic Inspection and Maintenance of High-Voltage Power Lines

2015· article· en· W2022427525 on OpenAlexaffabout
Nicolas Pouliot, Pierre-Luc Richard, Serge Montambault

Bibliographic record

VenueIEEE Power and Energy Technology Systems Journal · 2015
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsTeleoperationModular designMechatronicsSystems engineeringRobotDomain (mathematical analysis)EngineeringPower (physics)High voltageGridComputer scienceElectrical engineeringEmbedded systemVoltageArtificial intelligenceOperating system

Abstract

fetched live from OpenAlex

Historically, the inspection and maintenance of high-voltage power lines have been performed by linemen using various traditional means. In recent years, the use of robots appeared as a new and complementary method of performing such tasks, as several initiatives have been explored around the world. Among them is the teleoperated robotic platform called LineScout Technology, developed by Hydro-Québec, which has the capacity to clear most obstacles found on the grid. Since its 2006 introduction in the operations, it is considered by many utilities as the pioneer project in the domain. This paper's purpose is to present the mobile platform design and its main mechatronics subsystems to support a comprehensive description of the main functions and application modules it offers. This includes sensors and a compact modular arm equipped with tools to repair cables and broken conductor strands. This system has now been used on many occasions to assess the condition of power line infrastructure and some results are presented. Finally, future developments and potential technologies roadmap are briefly discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.002

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 head0.009
GPT teacher head0.214
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations126
Published2015
Admission routes2
Has abstractyes

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