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Record W2900111440 · doi:10.1109/tia.2018.2879790

Universal Input AC Three-Phase Power Factor Correction With Adaptive Intermediate Bus Voltage to Optimize Efficiency

2018· article· en· W2900111440 on OpenAlexaff
Hamidreza Hafezinasab, Wilson Eberle, Deepak Gautam, Chris Botting

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2018
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsDelta-Q Technologies (Canada)University of British Columbia
Fundersnot available
KeywordsPower factorAC powerVoltagePower (physics)Computer scienceThree-phaseFactor (programming language)Phase (matter)Control theory (sociology)Electrical engineeringElectronic engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This paper proposes an adaptive intermediate bus voltage solution to optimize efficiency in a universal three-phase ac input (200-480 V) cascaded buck-follows-boost power factor corrected (PFC) converter with a 400-V dc output voltage. With this application and architecture, the output voltage of the boost converter needs to be higher than the peak ac input voltage to maintain PFC and regulation. The conventional approach would regulate the intermediate bus voltage to near 800-V dc; this allows for 480-V ac high line input, plus allowable overvoltage tolerance and margin for regulation, but it incurs heavy losses at low line input. This paper proposes to adaptively change the bus voltage between the boost and buck stages, based on the value of the ac input voltage, and the use of a relay to bypass the buck stage for low ac line input conditions in order to maximize efficiency. A loss analysis is included to show the significant loss savings and efficiency improvement using the proposed method. Experimental results are presented for a 5-kW silicon-carbide-based prototype. The proposed method demonstrates up to a 4.4 percentage point increase in efficiency (220-W decrease in loss) at low ac line input compared to the conventional PFC approach with an 800-V dc intermediate bus voltage.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.953
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.241
Teacher spread0.225 · 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.

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

Citations7
Published2018
Admission routes1
Has abstractyes

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