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Record W2989550445 · doi:10.1109/tie.2019.2949537

Modulation and Voltage Balancing of a Five-Level Series-Connected Multilevel Inverter With Reduced Isolated Direct Current Sources

2019· article· en· W2989550445 on OpenAlexaff
Apparao Dekka, Omid Beik, Mehdi Narimani

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2019
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster UniversityLakehead University
Fundersnot available
KeywordsSeries and parallel circuitsCapacitorInverterVoltageTransformerModulation indexElectronic engineeringWaveformPulse-width modulationEngineeringComputer scienceGrid-tie inverterElectrical engineeringMaximum power point tracking

Abstract

fetched live from OpenAlex

The series connection of low-power modules is an alternative solution to realize a multilevel inverter. Unlike a cascade connection, the series connection approach significantly reduces the number of isolated dc sources, thereby the design complexity, cabling of the transformer, and overall system cost become low. In this article, the operation of the series-connected multilevel inverter is presented for a five-level operation, and it is realized by using two three-level half-bridge diode clamp converter modules per phase. Hence, each phase of the series-connected multilevel inverter requires a single isolated dc source. In the series-connected multilevel inverter, the net dc-bus voltage will be equally distributed between four dc-bus capacitors. Therefore, the converter generates a multilevel voltage waveform with uniform steps and ensures equal voltage stress on the semiconductor devices. To achieve these objectives, a space vector pulsewidth modulation scheme with an additional voltage balancing approach is employed. The feasibility of series-connected multilevel inverter is validated through simulations and a scaled-down laboratory prototype under steady-state and transient conditions.

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: Empirical
Teacher disagreement score0.638
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 integrity0.0000.001
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 head0.020
GPT teacher head0.207
Teacher spread0.188 · 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

Citations30
Published2019
Admission routes1
Has abstractyes

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