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Bipolar and unipolar schemes for confined band variable switching frequency PWM based inverter

2021· article· en· W3092565262 on OpenAlexaff
Hussain Attia, Hang Seng, Ahmad Elkhateb

Bibliographic record

VenueInternational Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsQueen's University
FundersEngineering and Physical Sciences Research Council
KeywordsPulse-width modulationTotal harmonic distortionInverterHarmonicsMATLABComputer scienceControl theory (sociology)Modulation (music)Electronic engineeringVoltagePhysicsEngineeringElectrical engineeringAcoustics

Abstract

fetched live from OpenAlex

The single phase inverter performance through the unipolar and bipolar strategies has been previously analyzed based on the constant switching frequency pulse width modulation (CSFPWM). However, the confined band variable switching frequency PWM (CB-VSFPWM) is currently proposed as a new variable switching frequency PWM technique through unipolar strategy to facilitate the design of high order filter, to reduce the switching losses, and to reduce the current total harmonics distortion (THD) as well. To evaluate the performance of a single phase inverter based on the CBVSFPWM through bipolar strategy, this paper presents a comparative study of the CB-VSFPWM based inverter performance using the unipolar PWM and the bipolar PWM strategies. The study adopts MATLAB/Simulink to simulate the inverter and to analyze the simulation results in terms of harmonics spectrum, total harmonic distortion (THD), and fundamental components. The analysis of the study results gives an indication about the appropriate type of CB-VSFPWM strategy (unipolar PWM or bipolar PWM) to guarantee the desired performance of the connected inverter in terms of the electrical grid standards like THD, and harmonics spectrum of the inverter current.

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 categoriesnone
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 score0.903
Threshold uncertainty score0.895

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 head0.004
GPT teacher head0.197
Teacher spread0.193 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2021
Admission routes1
Has abstractyes

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