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Record W2052231831 · doi:10.1109/ccece.2010.5575110

A Permanent Magnet Synchronous Motor drive employing a three-level Very Spars Matrix Converter with soft switching and SVM hysteresis current control

2010· article· en· W2052231831 on OpenAlexaff
Mohamed Aner, Nacer Benaifa, Ed Nowicki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsControl theory (sociology)Space vector modulationInductorVector controlPulse-width modulationThree-phaseComputer scienceVoltageMotor driveInverterEngineeringInduction motorElectronic engineeringElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

Proposed in this paper is a three-phase three-level Very Sparse Matrix Converter (VSMC) fed Permanent Magnet Synchronous Motor (PMSM). Such a system may be designed to draw and sinusoidal input current, have high efficiency and is free of a DC-link energy storage element (inductor and/or capacitor). As discussed in this paper, the three-level inverter output stage produces reduced distortion waveforms with low switch voltage stress. Controlling the PMSM by hysteresis current control (HCC) based vector control is an efficient method especially when the flux has a sinusoidal distribution. However, a hard switching problem may arise. In this paper, a new soft switching HCC technique based on space vector modulation (SVM) combined with field oriented control (FOC) is proposed. This technique uses the zero voltage vector (ZVV) to generate the matrix converter switching signals. The proposed technique employs three modes of compensation based directly on the motor current error making the technique reliable in operation. Simulation results of three-phase three-level VSMC drives are obtained to verify this proposed switching technique.

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 score0.959
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 head0.011
GPT teacher head0.210
Teacher spread0.200 · 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
Published2010
Admission routes1
Has abstractyes

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