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Reactive Power Modulation Strategy of a Single-stage Buck-boost-type Inverter

2020· article· en· W3095025863 on OpenAlexafffund
Ken King Man Siu, Carl Ngai Man Ho

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Manitoba
FundersCanada Research Chairs
KeywordsInverterAC powerPower factorGrid-tie inverterComputer scienceElectronic engineeringModulation indexPower (physics)Control theory (sociology)VoltageEngineeringMaximum power point trackingElectrical engineeringControl (management)Physics

Abstract

fetched live from OpenAlex

The paper presents a reactive power control methodology for a recently proposed single-stage buck-boost inverter. With the use of the recently proposed topology and the proposed modulation method, the resulted system can achieve efficient power conversion with a wide input voltage range, reactive power injection, and low leakage current. The designed system is able to work under various operation modes, including, capacitive operation, inductive operation, and inverter operation. Over the four-quadrant operation, all the time, only two switches are required to conduct in the main current path and only one of the main switches is in high-frequency operation in each quadrant operation. Therefore, while supporting reactive power injection, the constructed power inverter is able to maintain in simple structure and with effective power transmission. In this paper, detailed system analysis is presented which includes steady-state performance, system stability, and design criteria. The presented control strategy is experimentally verified in an 800 W inverter platform, where the performance is shown with a good agreement with the theoretical 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 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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.220
Teacher spread0.177 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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