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Record W2923926185 · doi:10.3390/app9061198

Analysis and Design of A PMQR-Type Repetitive Control Scheme for Grid-Connected H6 Inverters

2019· article· en· W2923926185 on OpenAlexaff
Xiaohui Yang, Peiyun Liu, Shaoping Xu, Shichao Liu

Bibliographic record

VenueApplied Sciences · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsCarleton University
FundersNational Natural Science Foundation of ChinaJiangxi Provincial Department of Science and TechnologyNational Science Foundation
KeywordsRepetitive controlControl theory (sociology)HarmonicsController (irrigation)GridComputer scienceCompensation (psychology)InverterTransient (computer programming)Three-phaseControl (management)Control systemEngineeringMathematicsVoltageElectrical engineering

Abstract

fetched live from OpenAlex

There exist several challenges in the implementation of proportional multiple quasi-resonant (PMQR) control strategies in single-phase grid-connected H6 inverters, such as high computational costs and design complexity. To overcome these challenges, this paper proposes a proportional multiple quasi-resonant (PMQR)-type repetitive control (PMQR-type RC) scheme for single-phase grid-connected H6 inverters. In the control scheme, a repetitive controller and a proportional controller run in parallel. The repetitive controller is to improve the steady-state harmonics compensation ability, while the proportional controller can enhance the transient performance of the system. Both theoretical stability analysis and detailed design steps regarding the proposed control scheme are introduced. Finally, comparison results on a typical single-phase grid-connected H6 inverter with LC filter under a variety of control methods verify the capability of suppressing harmonics and the robust performance of the proposed control strategy against grid disturbances.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.696
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.008
GPT teacher head0.193
Teacher spread0.185 · 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
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

Citations1
Published2019
Admission routes1
Has abstractyes

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