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Record W1998398994 · doi:10.1109/tec.2014.2349653

Development of Power Interface With FPGA-Based Adaptive Control for PEM-FC System

2014· article· en· W1998398994 on OpenAlexafffund
Alben Cardenas, Kodjo Agbossou, Nilson Henao

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2014
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsInsulated-gate bipolar transistorRippleInverterConvertersController (irrigation)Interface (matter)Field-programmable gate arrayTransient (computer programming)Control systemTorque rippleComputer scienceGate arrayElectronic engineeringElectrical engineeringEngineeringVoltageEmbedded systemInduction motorDirect torque control

Abstract

fetched live from OpenAlex

This paper presents the development of a power interface for a fuel cell (FC) system supplying an ac load. The proposed system comprises a two stages conditioning system, including a switched capacitor dc-dc boost converter and a full bridge insulated-gate bipolar transistor (IGBT) single-phase inverter (dc-ac converter). A fully digital control structure is proposed to manage both power converters using the same control device. The control strategy takes advantage of the parallelism of the field programmable gate arrays (FPGA) technology to share in real-time the controller's information addressing the common problem of low-frequency ripple of FC current when supplying ac loads. The control system has been implemented and evaluated by co-simulation, and by experimentation with a proton exchange membrane fuel cell (PEM-FC) system. The results show that the proposed system allows a safe operation of the FC by limiting the current ripple under 3%. These results also confirm that the transient response of the conversion system permits to correctly supply the ac load.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.975
Threshold uncertainty score0.482

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 head0.005
GPT teacher head0.169
Teacher spread0.164 · 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

Citations22
Published2014
Admission routes2
Has abstractyes

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