MétaCan
Menu
Back to cohort

Digital Twin System for Transformer Station Based on Cloud-edge Collaboration Architecture

2022· article· en· W4328030238 on OpenAlexaff
Yuying Xue, Shen Yun, Huibin Duan, Yaqi Song, Mohamed Cheriet

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicDigital Transformation in Industry
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsTransformerCloud computingArchitectureDistribution transformerComputer scienceEngineeringElectrical engineeringComputer networkOperating systemVoltage

Abstract

fetched live from OpenAlex

The transformer station is an important link in the transmission process of the power grid, digital twin is a new way to develop intelligent transformer, which can effectively improve the safety of the transformer station. In this paper, the digital twin technology is applied to the transformer station. Firstly, the basic architecture of digital twin is analyzed, two network deployment schemes are proposed according to the requirements of three-dimensional model construction, information transmission, and two-way interaction to provide a stable infrastructure for digital twin. Then, according to the characteristics and needs of the transformer station, a digital twin transformer station is built based on the cloud-edge collaboration architecture, and the capabilities of the cloud and the edge are defined. Finally, a digital twin transformer station model is built from multiple perspectives. An all-weather detection model is proposed for the safety requirements of the transformer station, and the detection content and information interaction are determined.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.988
Threshold uncertainty score0.555

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 head0.009
GPT teacher head0.209
Teacher spread0.200 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same topicDigital Transformation in IndustryFrench-language works237,207