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Record W2558186615 · doi:10.1109/isie.2016.7744993

Capacitance reduction in a single phase Quasi Z-Source Inverter using a hysteresis current controlled active power filter

2016· article· en· W2558186615 on OpenAlexafffund
Siddhartha A. Singh, Najath Abdul Azeez, Sheldon S. Williamson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsOntario Tech University
FundersNatural Sciences and Engineering Research Council of CanadaEnergy Council of Canada
KeywordsInverterCapacitorRippleElectrolytic capacitorGrid-tie inverterTopology (electrical circuits)Electrical engineeringPower factorZ-source inverterThree-phaseFilter capacitorInductorLC circuitElectronic engineeringEngineeringVoltageMaximum power point tracking

Abstract

fetched live from OpenAlex

One of the major issues in a single phase inverter topology is the 120Hz ripple which needs large decoupling capacitors. Big bulky electrolytic capacitors can be an issue in applications where compact size and high power density are requirements such as solar converter chargers. In z source inverter systems, the design of the impedance network is critical. In single phase z source inverter systems, the z-source network can get quite bulky if the second harmonic power fluctuations are not compensated. In this paper an active power filter (APF) has been analyzed for the DC side of the Quasi Z-Source Inverter(qZSI) Topology and hysteresis current control technique has been adopted to eliminate the voltage fluctuations in the DC side of the inverter. As a result the impedance network has been designed with the values as low as three phase z-source inverter systems and the capacitor values are low enough to replace the bulky capacitors by film capacitors, which in turn increases the life and power density of such inverter systems. The other advantage of this DC side APF being the control is simpler than an AC side APF.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.830
Threshold uncertainty score0.718

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 head0.033
GPT teacher head0.254
Teacher spread0.221 · 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 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

Citations26
Published2016
Admission routes2
Has abstractyes

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