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Record W4220849341 · doi:10.1109/sii52469.2022.9708835

All-weather autonomous inspection robot for electrical substations

2022· article· en· W4220849341 on OpenAlexaffabout
Philippe Dandurand, Julien Beaudry, Camille Hébert, P. Mongenot, Jeremie Bourque, Samuel Hovington

Bibliographic record

Venue2022 IEEE/SICE International Symposium on System Integration (SII) · 2022
Typearticle
Languageen
FieldEngineering
TopicPower Line Inspection Robots
Canadian institutionsUniversité de SherbrookeHydro-Québec
Fundersnot available
KeywordsRobotTransformerCircuit breakerComputer scienceElectric power systemElectric power transmissionReliability engineeringSoftwareEngineeringElectric powerSystems engineeringReal-time computingControl engineeringPower (physics)Electrical engineeringArtificial intelligenceVoltage

Abstract

fetched live from OpenAlex

Electric utilities that operate power transmission networks must periodically inspect their numerous substations which contain diversified types of equipment (power transformers, circuit breakers, etc.). Completing all the inspection tasks is challenging for a geographically distributed network like the one operated by Hydro-Quebec. Using remote robot systems´ to accomplish those tasks is beneficent in terms of personnel safety, operational efficiency and asset management. The robot system must, however, be capable of operating reliably in harsh winter conditions. This paper presents a new robot system developed at Hydro-Quebec capable of addressing these specific´ inspection needs. It provides an overview of the system and its main components followed by a more detailed description of the software and algorithms required for autonomous operation of such an inspection robot, particularly in winter 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 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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.049
Threshold uncertainty score0.098

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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

Citations15
Published2022
Admission routes2
Has abstractyes

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