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Record W3151438770 · doi:10.24295/cpsstpea.2021.00006

Switched Capacitor Based Cascaded Half-Bridge Multilevel Inverter With Voltage Boosting Feature

2021· article· en· W3151438770 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCPSS Transactions on Power Electronics and Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersVelux Fonden
KeywordsTopology (electrical circuits)CapacitorInverterBoosting (machine learning)InductorComputer scienceVoltageElectronic engineeringEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Cascaded multilevel inverter (CMI) topology is prevalent in many applications. However, the CMI requires many switches and isolated dc sources, which is the main drawback of this type of inverter. As a result, the volume, cost and complexity of the CMI topology are increased and the efficiency is deteriorated. This paper thus proposes a switched-capacitor-based multilevel inverter topology with half-bridge cells and only one dc source. Compared to the conventional CMI, the proposed inverter uses almost half the number of switches, while maintaining a boosting capability. Additionally, the main drawback of switched-capacitor multilevel inverters is the capacitor inrush current. This problem is also averted in the proposed topology by using a charging inductor or quasi-resonant capacitor charging with a front-end boost converter. Simulation results and lab-scale experimental verifications are provided to validate the feasibility and viability of the proposed inverter 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.

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 score0.989
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 integrity0.0000.001
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 head0.009
GPT teacher head0.202
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