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Record W2947273471 · doi:10.1109/apec.2019.8722262

Masterless Interleaving Scheme for Parallel-Connected Inverters Operating with Variable Frequency Hysteretic Current-Mode Control

2019· article· en· W2947273471 on OpenAlexaff
Samantha K. Murray, Miad Nasr, Mojtaba Ashourloo, Olivier Trescases

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsInterleavingInverterControl theory (sociology)InductorHarmonicsComputer scienceController (irrigation)EMIElectronic engineeringFilter (signal processing)VoltageEngineeringElectromagnetic interferenceElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This paper describes a new masterless interleaving scheme for parallel sub-inverters operating in variable frequency Hysteretic Current-Mode Control (HCMC). The proposed digital control scheme enables the development of fault-tolerant, modular, efficient, and power dense inverters. A feed-forward controller is used to instantaneously correct for phase error at each switching cycle, while a compensator corrects for the systematic difference in sub-inverter switching frequencies caused by inductor mismatch. The control is verified by simulation up to 2 kW in an inverter system consisting of three parallel sub-inverters. Experimental results are presented for the same system operating in Boundary Conduction Mode (BCM) at 895 W. Activation of the interleaving controller shows a reduction in peak current into the system EMI filter, while maintaining the soft-switching benefits of BCM operation. The proposed interleaving scheme reduces the magnitude of the lowest switching frequency harmonics by a peak of 35 dB and an average of approximately 10 dB, which is demonstrated by comparing the inverter system's current spectrum with and without interleaving.

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 score0.916
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
Bibliometrics0.0000.000
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 head0.009
GPT teacher head0.213
Teacher spread0.203 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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