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Low-Speed Operation of a New Four-Level Multilevel Inverter fed Medium-Voltage Drive

2022· article· en· W4361792130 on OpenAlexafffund
Hoang Le, Apparao Dekka, Deepak Ronanki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsLakehead University
FundersLakehead University
KeywordsCapacitorPulse-width modulationRippleVoltageTorque rippleInduction motorStatorMotor driveEngineeringElectrical engineeringControl theory (sociology)Electronic engineeringComputer scienceDirect torque control

Abstract

fetched live from OpenAlex

Multilevel inverters became an integral part of medium-voltage motor drives. The newly developed multilevel inverters are designed with floating capacitors to generate a multilevel voltage waveform, while fulfilling the motor-side requirements. However, the low-speed operation of motor drive leads to a larger ripple in the floating capacitors voltage. These ripples put more stress on floating capacitors and switching devices, and subsequently cause their failure over a long run. The voltage ripples also affect the quality of motor stator currents and electromagnetic torque. Therefore, the minimization of ripple in floating capacitors voltage is a key aspect of developing a high-performance motor drive. First, a new medium-voltage motor drive for high-power applications is developed. The developed motor drive is fed by a new four-level multilevel inverter. Second, a simple voltage balancing method is developed to balance the floating capacitors voltage in the proposed multilevel inverter. Finally, a modified carrier pulse width modulation scheme is proposed to reduce the ripple in floating capacitors voltage at the low-speed operation of a motor drive. The proposed motor drive performance with a modified carrier pulse width modulation is validated through simulations. Also, the performance comparison with in-phase disposition pulse width modulation scheme is presented.

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 categoriesInsufficient 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: none
Teacher disagreement score0.921
Threshold uncertainty score0.996

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)0.0040.000

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 head0.039
GPT teacher head0.224
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

Citations3
Published2022
Admission routes2
Has abstractyes

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