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Record W4296708067 · doi:10.1109/tia.2022.3208221

Boost Packed E-Cell: A Compact Multilevel Converter for Power Quality Ancillary Services

2022· article· en· W4296708067 on OpenAlexafffund
Mohammad Babaie, Kamal Al‐Haddad

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitorHarmonicsVoltageWaveformElectronic engineeringAC powerPower factorFault (geology)EngineeringElectrical engineeringPower (physics)Computer scienceTopology (electrical circuits)Physics

Abstract

fetched live from OpenAlex

This paper proposes Boost Packed E-Cell (BPEC) as an affordable Compact Multilevel Converter (CMLC) for the power quality ancillary services. The BPEC is a transformerless bidirectional CMLC topology and can generate symmetrical and asymmetrical multilevel voltage waveforms with 5-to-11-level resolution using only three low-voltage dc capacitors and eight power switches. Thanks to the serial expansion of the two dc capacitors, BPEC has two dc-links, which means two voltage sensors are enough to control three dc capacitors. Despite other CMLCs, BPEC does not need a fault detector and inherently generates a symmetrical 5-level or an asymmetrical 7-level voltage waveform in the event of a fault of the middle switches or their gate drivers. As a case study, a single-phase Compact Active Power Filter (CAPF) is designed in this paper based on the BPEC to compensate for harmonics and reactive power caused by unknown non/linear loads, simultaneously. Finite control set predictive control strategy is also adopted based on the switched model of the power system containing the grid, unknown non/linear loads, and the BPEC to address the grid power quality concerns. The experiments performed by a prototype including the BPEC power board, MicroLabBox, OPAL-RT OP8662, and Chroma 61086 verify the merits of the proposed CAPF in practice.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.952
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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.026
GPT teacher head0.263
Teacher spread0.237 · 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 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

Citations15
Published2022
Admission routes2
Has abstractyes

Explore more

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