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Record W2552745720 · doi:10.1109/tpel.2016.2627049

Enhanced Instantaneous Power Theory for Control of Grid Connected Voltage Sourced Converters Under Unbalanced Conditions

2016· article· en· W2552745720 on OpenAlexafffund
Allan A. Montanari, A.M. Gole

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2016
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsConvertersControl theory (sociology)Transient (computer programming)Power (physics)VoltageVoltage sourceTransformation (genetics)AC powerSequence (biology)GridPower controlEngineeringComputer scienceControl (management)MathematicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Undesirable power oscillations take place in grid-connected voltage sourced converters (VSC) during unbalanced conditions. This problem becomes more difficult and pronounced during transients which include zero sequence content flowing through the ac/dc interface. This paper introduces a method especially designed to control the instantaneous power in voltage sourced converters operating under unbalanced transient scenarios with positive, negative, and zero sequence content. The control is based on an adaptive transformation that instantaneously adjusts itself to the dynamic voltage conditions. The method allows control over constant and oscillating terms of the instantaneous three-phase power. It is applied to grounded three-wire and four-wire schemes, especially accommodating zero sequence unlike previous approaches. The technique expands the application of the p-q theory, which is attached to the αβ0 transformation. The effectiveness of the proposed control approach and the proposed power conditioning scheme was demonstrated using electromagnetic transient simulation of a VSC connected to an ac system.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.186
Teacher spread0.183 · 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

Citations46
Published2016
Admission routes2
Has abstractyes

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