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Record W2114074088 · doi:10.1109/tpel.2008.925414

A Minimally Switched Control Algorithm forThree-Phase Four-Leg VSI Topology toCompensate Unbalanced and Nonlinear Load

2008· article· en· W2114074088 on OpenAlexaff
Piyush Lohia, Mahesh K. Mishra, Kottaisamy Karthikeyan, Krishna Vasudevan

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2008
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsControl theory (sociology)MATLABNonlinear systemInverterTopology (electrical circuits)Three-phaseVoltagePhase (matter)Computer sciencePulse-width modulationEngineeringControl (management)Physics

Abstract

fetched live from OpenAlex

In this paper, a minimally switched control algorithm for a three-phase four-leg voltage source inverter topology is proposed for compensation of unbalanced and nonlinear loads. An optimized control technique is necessary for the three-phase four-leg topology, otherwise it requires higher switching operations of the switches in the voltage source inverter for tracking three-phase and neutral currents through conventional hysteresis band control. In the paper, a control technique has been formulated and its effect of reducing the switching frequency over a period of time has been studied and verified. A simulation study of three-phase, four-wire compensated system is carried out using Matlab/Simulink to validate the proposed method. Detailed experimental results presented further confirm the efficacy of the proposed control.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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 designBench or experimental
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

Citations82
Published2008
Admission routes1
Has abstractyes

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