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Grid-Tied Packed E-Cell Inverter with Active Capacitor Voltage Balancing

2020· article· en· W3098530118 on OpenAlexaff
Mohammad Sharifzadeh, Mohammad Khenar, Kamal Al‐Haddad

Bibliographic record

VenueIECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society · 2020
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversité du Québec en OutaouaisÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsCapacitorInverterGridVoltageTopology (electrical circuits)MATLABComputer scienceElectrical engineeringControl theory (sociology)Electronic engineeringEngineeringControl (management)Mathematics

Abstract

fetched live from OpenAlex

This paper proposed a PI based control technique for grid-tied application of recent introduced compact multilevel inverter so called as Packed E-Cell. The nine-level Packed E-Cell (PEC9) benefits the advantage of single auxiliary and main dc source where the dc capacitors are placed in a horizontal extension on the single auxiliary dc link where simultaneous charging and discharging with effective redundant switching states are provided. Therefore, PEC9 is an active capacitor voltage-balancing converter where the capacitors voltages are adjusted to the right amplitude using only redundant switching states. Thanks to this prominent advantage, PEC9 is an appropriate nine-level topology for grid-tied inverter as the capacitors voltages are not required to be involved in grid-tied control technique. Therefore, it was shown that PEC9 inverter can be perfectly controlled with a simple PI technique for grid-tied configuration. The proposed PI-based technique has been tested using Matlab-Simulink on grid-tied PEC9 inverter under both steady-state and dynamical operation.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
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 science0.0010.000
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 head0.024
GPT teacher head0.188
Teacher spread0.164 · 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.

Study designNot applicable
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

Citations6
Published2020
Admission routes1
Has abstractyes

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