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Model Predictive Control of a New Five-Level Inverter for Medium Voltage Applications

2023· article· en· W4390416663 on OpenAlexaff
Hoang Le, Apparao Dekka, Deepak Ronanki

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsLakehead University
FundersCentral Power Research Institute
KeywordsInverterModel predictive controlVoltageCapacitorControl theory (sociology)Computer scienceGrid-tie inverterTopology (electrical circuits)Control (management)Electronic engineeringEngineeringElectrical engineeringMaximum power point tracking

Abstract

fetched live from OpenAlex

This paper introduces a new five-level inverter for medium-voltage (MV) applications. The proposed topology has fewer components (active and passive elements) and does not require any isolated DC sources in comparison to the existing five-level inverters. In the proposed inverter, each flying capacitor (FC) voltage needs to be regulated at one-fourth of the net DC-bus voltage to generate a five-level voltage at the output terminals of the inverter. To control the FC voltages along with the load currents, a model predictive control (MPC) technique is developed. The discrete-time models of the proposed five-level inverter are presented to implement the MPC. The experimental studies are presented to verify the operation and performance of the proposed five-level inverter with an MPC.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.995
Threshold uncertainty score0.481

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 science0.0000.000
Research integrity0.0000.000
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 head0.039
GPT teacher head0.244
Teacher spread0.205 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2023
Admission routes1
Has abstractyes

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