MétaCan
Menu
Back to cohort
Record W2772978916 · doi:10.1109/iecon.2017.8217161

Three-phase interleaved semi-controlled PFC converter for aircraft application

2017· article· en· W2772978916 on OpenAlexaff
Sivanagaraju Gangavarapu, Akshay Kumar Rathore

Bibliographic record

VenueIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsConvertersVoltageCurrent (fluid)Control theory (sociology)Power factorCurrent loopBoost converterComputer sciencePower (physics)Electronic engineeringEngineeringControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

In more electric aircraft (MEA), three phase PFC rectifiers of several kilowatts are required. In this paper, analysis and design of a single stage three phase PFC interleaved half controlled boost converter for aircraft application is presented. The proposed converter uses the voltage follower approach i.e it is operated in Discontinuous Conduction Mode (DCM). This avoids the inner current control loop which further eliminates the sensing of current through current sensors. This makes the system more reliable and robust. This approach uses only one voltage control loop for output voltage regulation. A two stage approach is also proposed to improve the input current quality of the converter. To validate the analysis, the proposed converters are simulated in PSIM. A comparison between the single stage and two stage converters in terms of efficiency, power factor and input current quality are presented. The results show that the input current quality is high in two stage approach with slight compromise in efficiency.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.042
GPT teacher head0.286
Teacher spread0.244 · 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 designBench or experimental
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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics SocietySame topicAdvanced DC-DC ConvertersFrench-language works237,207