MétaCan
Menu
Back to cohort
Record W3036758573 · doi:10.18280/jesa.530212

An Improved Single Phase Self-balancing Switched Capacitor Based Step-up Nine Level Inverter

2020· article· en· W3036758573 on OpenAlexvenueno aff
Gouse Shaik, Venkatesan Mani, Subbarao Mopidevi

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2020
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsnot available
Fundersnot available
KeywordsSwitched capacitorPhase (matter)InverterCapacitorControl theory (sociology)Computer scienceElectrical engineeringEngineeringVoltageChemistryControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

The improved single phase switched capacitor based nine level inverter is presented in this article.A low DC input voltage is transformed into AC and boost up to the high output voltage without any Boost converter, inductors, transformers.The self-balancing process is involved in the states of charging and discharging of the capacitors.The presented topology does not have any H-bridge configurations which result in the low Total Standing Voltage (TSV).The Phase Disposition Carrier based PWM (PDCPWM) control technique is applied to the presented nine level inverter.The conduction loss, switching loss, efficiency and capacitor ratings are analyzed mathematically.The comparative analysis of the number of semiconductor switches, Total Standing Voltage, Peak Inverse Voltage between the presented topology and existing topology is explained in detail.Finally, using MATLAB/SIMULINK the proposed nine level inverter is simulated to realize the performance of the presented topology.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.035
GPT teacher head0.245
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 source (direct Gemma or distilled Codex), 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

Citations0
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicMultilevel Inverters and ConvertersFrench-language works237,207