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Record W2965989440 · doi:10.1109/compel.2019.8769636

Multi-Level Voltage Source Parallel Inverters using Coupled Inductors

2019· article· en· W2965989440 on OpenAlexaff
Sukhjit Singh, John Salmon

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsInductorVoltageElectrical engineeringComputer scienceVoltage sourceElectronic engineeringEngineering

Abstract

fetched live from OpenAlex

Inverter legs can be connected in parallel using coupled inductors to produce multi-level PWM output voltages with PWM frequencies higher than the inverter switching frequency. Techniques are described for connecting the coupled inductor windings to parallel inverter legs that lower the effective series inductance at the system output terminals. High-quality multi-level PWM output voltages result with low fundamental voltage drops across the output impedance. The proposed PWM technique can be implemented using either multi-limb or a modular system where individual magnetic cores are connected to each inverter leg. The low output impedance means that high-frequency fundamental voltages can be generated using a relatively low carrier to fundamental ratio. Carrier/reference signal manipulation techniques are presented that improve the quality of the multi-level output voltages and that can be easily implemented in digital hardware. The inverter structure is presented for a 3-phase ac output that can be used as a motor drive system or a utility connected PWM rectifier. For a 3-phase system, 7 and 9 level line voltages can be obtained using 3 of 4 inverter legs per phase respectively. The operation of the system is demonstrated using simulations and experimental results.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.832
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)0.0010.001

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 head0.047
GPT teacher head0.232
Teacher spread0.185 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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