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Record W3082690515 · doi:10.1109/tia.2020.3020534

On the Ground Potentials and Grounding Circuits of Transformerless Grid-Connected Multilevel Power Electronic Converters

2020· article· en· W3082690515 on OpenAlexafffund
S. A. Saleh, Ahmed Al‐Durra, Razzaqul Ahshan

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation Foundation
KeywordsGroundElectrical engineeringConvertersElectronic circuitCapacitorTransformerEngineeringPower electronicsElectronic engineeringVoltageGridTotal harmonic distortionComputer science

Abstract

fetched live from OpenAlex

Transformerless grid-connected power electronic converters (PECs) are used in photovoltaic systems, motor drives, and solid-state power transformers. In these applications, transformerless grid-connected multilevel PECs can reduce harmonic distortions, increase power, and voltage ratings. This article aims to develop models for the ground potentials in these PECs, and to design grounding circuits for them. The models for ground potentials are developed using the common-mode voltages across each leg of the multilevel PEC. Grounding circuits for transformerless grid-connected multilevel PECs are designed using frequency selective circuits to limit ground potentials, and block ground currents from flowing through grounding of the host grid. The developed models, and grounding circuits are evaluated for transformerless grid connected, diode clamped, flying capacitor, and cascaded H-bridge multilevel PECs under different operating conditions. Test results demonstrate the significant advantages of the frequency-selective grounding for transformerless grid-connected PECs. Observed advantages include reduced harmonic distortion, minimized ground potentials, and improved efficiency.

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: none
Teacher disagreement score0.845
Threshold uncertainty score0.909

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 head0.022
GPT teacher head0.215
Teacher spread0.194 · 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

Citations20
Published2020
Admission routes2
Has abstractyes

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