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Optimal Low Switching Frequency PWM Pattern for a Three-Level Neutral Point Clamped (NPC) Inverter

2023· article· en· W4378843247 on OpenAlexaff
Aathira Karuvaril Vijayan, Battur Batkhishig, Mehdi Narimani, Ali Emadi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTotal harmonic distortionPulse-width modulationInverterControl theory (sociology)VoltagePower (physics)Modulation (music)Computer scienceSwitching timeHarmonicWaveformElectronic engineeringTopology (electrical circuits)EngineeringPhysicsElectrical engineeringControl (management)Acoustics

Abstract

fetched live from OpenAlex

This paper proposes a mechanism of development of synchronous optimal pulse width modulation (PWM) generation for control of medium-voltage motor drives using multilevel inverters at low switching frequency. Multilevel inverters enable operation with multiple dc-link voltages while lowering total harmonic distortion (THD). Thermal losses limit the maximum switching frequency in high-power applications. As a result, the output waveforms are highly distorted. Synchronous optimal PWM (SOP) control allows for a low maximum switching frequency without sacrificing THD. The switching losses of power semiconductor devices are reduced when the switching frequency is low. A detailed explanation of an optimal control procedure is provided. The paper investigates the implementation of the SOP scheme in a three-level inverter topology and is compared with a two-level inverter. Also, the performance of SOP and in-phase disposition (IPD) modulation schemes are evaluated on a three-level neutral point clamped inverter with a switching frequency of 420Hz. The findings indicate that SOP improves the results of IPD for low switching frequencies. This demonstrates that the SOP is very useful in high power applications, resulting in a significant reduction in the use of bulky and expensive filtering elements.

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: none
Teacher disagreement score0.971
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 head0.036
GPT teacher head0.233
Teacher spread0.198 · 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
Published2023
Admission routes1
Has abstractyes

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