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Record W4415169177 · doi:10.1049/icp.2025.1450

Innovating for safety: the journey of partial discharge measurement tools at Hydro-Quebec

2025· article· en· W4415169177 on OpenAlexaffabout
Lionel Reynaud, Lucas Malo Bélanger, Marc-André Magnan, Michel Trépanier

Bibliographic record

VenueIET conference proceedings. · 2025
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsHydro-Québec
Fundersnot available
KeywordsReliability (semiconductor)Software deploymentField (mathematics)Key (lock)Exploratory analysis

Abstract

fetched live from OpenAlex

Hydro-Quebec, a leading Canadian utility, has pioneered the development of partial discharge (PD) measurement tools to enhance the safety and reliability of its extensive underground medium voltage (MV) distribution network. Over the past thirty years, Hydro-Quebec has introduced multiple generations of PD detection devices. This article details the evolution of these innovations, emphasizing the challenges, technological advancements. We examine how these tools have adapted to meet the rigorous demands of field conditions and their role in identifying and preventing potential network failures. The article follows the progression of these tools from initial exploratory stages to the deployment of advanced, user-friendly devices for non-expert workers, highlighting the technical innovations, field applications, and their significant contribution to improving network reliability and worker safety.

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 score0.625
Threshold uncertainty score0.382

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 head0.043
GPT teacher head0.237
Teacher spread0.194 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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