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Record W7025175298

TSO perspectives on 40 years of GIS evolution, SF6 alternatives strategies and technical specifications recommendations

2022· article· en· W7025175298 on OpenAlexaboutno aff

Bibliographic record

VenueIRIS Research product catalog (Sapienza University of Rome) · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicGalaxies: Formation, Evolution, Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsWork (physics)SwitchgearGreenhouse gasGridFootprint
DOInot available

Abstract

fetched live from OpenAlex

During the last 40 years, Gas Insulated Switchgear technology has evolved. The years 2020’s, will see another major turn in the GIS evolution with a new type of technology using alternative gas to SF6. The four authors are employed from four different TSOs (Hydro Quebec, RTE, Terna and Statnett). Even though each company have different requirements (type of network, frequency, layout, architecture, and climatic environments), all four have a long history with GIS products, and share many similar problematics as well as common understanding of technical requirements. The paper will first be describing the evolutions of GIS through the last 40 years. Footprint evolution, gas mass and gas pressure evolutions, interface compatibility, leakage rate evolutions and gas compartment issues are presented and discussed. End-user issues related to the complexity to gas compartment and mechanical work in a vicinity of a close barrier fully pressurized, as well as non-intrusive diagnostic methods, are described. Reflections on the consequences of using SF6 alternatives are presented by the authors. Finally, the last chapter of this paper highlights the authors companies' requirements and recommendations regarding type testing and routine tests. The four authors also present companies' objectives and visions on how the grid decarbonization road maps will be implemented. Example on how to evaluate different SF6 free solutions are discussed. Concrete plans and actions to address the reduction of greenhouse gas emissions from authors companies' activities are described with the corresponding time frame.

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)0.011
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.167
Threshold uncertainty score0.332

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.009
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0020.004
Scholarly communication0.0100.009
Open science0.0030.004
Research integrity0.0060.006
Insufficient payload (model declined to judge)0.0380.007

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 head0.042
GPT teacher head0.296
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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