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Record W4401921686 · doi:10.18280/jesa.570409

The Influence of Non-Sinusoidal Power Supply on Single-Phase Transformer Performance

2024· article· fr· W4401921686 on OpenAlexvenueno aff
Omar Sh. Alyozbaky, Raghad Adeeb Othman

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2024
Typearticle
Languagefr
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTransformerSingle phaseSingle-phase electric powerElectrical engineeringSwitched-mode power supplyThree-phaseComputer scienceElectronic engineeringControl theory (sociology)VoltagePower factorEngineering

Abstract

fetched live from OpenAlex

Electrical transformers are generally designed to operate from sinusoidal sources.However, increased use of nonlinear loads, such as electric motor drives, lighting, fluorescent lamps, rectifiers, and computers, which generate high values of harmonics, makes this transformer operate in the nonlinear region.This paper discusses and focuses on the effect of the characteristics of a single-phase transformer when it is operated from a non-sinusoidal source with a non-linear load.An experimental 1 kVA single-phase transformer with a voltage of 220V and frequency of 50Hz with linear and nonlinear loads was considered.The results of this investigation show the losses increased by 25% when the transformer was fed from a sinusoidal power supply and the load was non-linear.In addition, the losses and THD for the current reached 40% and 12.4%, respectively, at nearfull load when the transformer was fed from a non-sinusoidal power supply and non-linear loads.The correlation between total harmonic distortion (THD) and these types of power supplies and loads was illustrated.The results showed that increasing the current harmonic distortion will lead to increased transformer losses and thus reduce their life expectancy.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.625
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.015
GPT teacher head0.264
Teacher spread0.248 · 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; both teacher heads agree on what is shown here.

Study designOther design
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
Published2024
Admission routes1
Has abstractyes

Explore more

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