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Record W2056459609 · doi:10.1109/tie.2013.2297291

Model Predictive Approach for a Simple and Effective Load Voltage Control of Four-Leg Inverter With an Output <inline-formula> <tex-math notation="TeX">$LC$</tex-math></inline-formula> Filter

2014· article· en· W2056459609 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIEEE Transactions on Industrial Electronics · 2014
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInverterControl theory (sociology)Model predictive controlVoltageFilter (signal processing)Capacitive sensingController (irrigation)Nonlinear systemReliability (semiconductor)MathematicsComputer scienceEngineeringControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

This paper presents a finite control set model predictive strategy and its application to the load voltage control of two-level four-leg inverters. The proposed approach uses the novel discrete-time model of the inverter and output LC filter in order to predict the variables to be controlled. These predictions are carried out for the 16 switching states of the inverter and are evaluated using a cost function. The switching state that forces the load voltages to be closest to their respective references is chosen and applied to the inverter. The behavior of the predictive controller has been investigated, and the changes to both inductive and capacitive filter parameters have been considered. In order to improve the reliability of the fourth leg as well as the overall inverter efficiency, a solution is proposed, which combines hardware and software reconfigurations. The feasibility of the proposed method is verified through simulation and experimental results considering single-/three-phase, balanced/unbalanced, and linear/nonlinear loads.

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.

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)0.001
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.849
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.021
GPT teacher head0.218
Teacher spread0.197 · 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