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Record W4378187812 · doi:10.14447/jnmes.v26i1.a03

Modelling of Symmetric Switched Capacitor Multilevel Inverter for High Power Appliances

2023· article· en· W4378187812 on OpenAlexvenueno aff
Devineni Gireesh Kumar, Nagineni Venkata Sireesha, DSNM Rao, Idamakanti Kasireddy, Bharath Kumar Narukullapati, Ranjith Kumar Gatla, P. Chandra Babu, S. Saravanan

Bibliographic record

VenueJournal of New Materials for Electrochemical Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsnot available
Fundersnot available
KeywordsSwitched capacitorInverterPower (physics)Electrical engineeringCapacitorComputer scienceAutomotive engineeringElectronic engineeringEngineeringPhysicsVoltage

Abstract

fetched live from OpenAlex

While designing the inverter, the total harmonic distortion (THD) in the output is a major concern to decide its performance.In order to calculate the total harmonic distortion (THD) of a symmetric modular multilevel inverter (MMI) with switched capacitors, a Harris hawk optimization (HHO) was used in this study.Utilizing symmetric and identical DC sources, the suggested modular multilevel inverter is designed.The suggested topology may be expanded up to many levels and utilizes fewer switches to provide 9 levels of output than conventi1al cascaded H-bridge multilevel inverters.Using a low-frequency switching control approach known as selective harmonic elimination pulse width modulation, the switches are less stressed and the inverter output's THD profile is improved.Additionally, the switching angles of the MMI have been optimized by solving the non-linear equations of the SHEPWM using the Harris Hawk Optimizer.Ant colony optimization (ACO) and particle swarm optimization (PSO), two alternative optimizers, were compared in terms of the THD of the output.This comparison shows that the HHO delivers a lower THD than other optimization techniques approximately near to 5%, as per the IEEE-519 standard, and is thus more highly advised.Finally, a hardware configuration for the suggested inverter is implemented to confirm the simulation findings.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.570
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.030
GPT teacher head0.240
Teacher spread0.210 · 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 designBench or experimental
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

Citations16
Published2023
Admission routes1
Has abstractyes

Explore more

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