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Design and Development of a Smart Tool for Distribution Transformer sizing Considering Plug-in Electric Vehicles Charging

2024· article· en· W4402475597 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicElevator Systems and Control
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsSizingDistribution transformerAutomotive engineeringPlug-inTransformerComputer scienceElectrical engineeringEngineeringVoltage

Abstract

fetched live from OpenAlex

The use of electric vehicles (EV) has exponentially grown within the past couple of years and it is expected to increase by 2050 causing a drastic increase in residential demand. Due to such an increase in demand, many existing distribution transformers servicing these residential homes may need either replacement or upgrade in their sizes to account for the home charging of these EVs. Choosing the correct economic transformer’s size helps electric utilities to benefit from such a significant capital investment. In this paper, a Smart Transformer Sizing Tool (STST) was developed to provide the correct economic size of transformers that ensure no overload as well as maintain the voltage quality. The results demonstrating the effectiveness of the proposed tool are presented and conclusions are drawn.

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.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.614
Threshold uncertainty score0.315

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 head0.014
GPT teacher head0.208
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

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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