MétaCan
Menu
Back to cohort
Record W2068064531 · doi:10.1109/iecon.2012.6389112

Comparison of orthogonal quantity generation methods used in single-phase grid-connected inverters

2012· article· en· W2068064531 on OpenAlexaff
Mohammad Ebrahimi, Hamid Reza Karshenas, Mohammad Hassanzahraee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsInterfacingGridComputer scienceReference frameControl theory (sociology)Phase (matter)SIGNAL (programming language)Three-phaseRotating reference frameExploitFrame (networking)Single phaseElectronic engineeringControl (management)EngineeringMathematicsElectrical engineeringTelecommunicationsVoltagePhysicsComputer hardware

Abstract

fetched live from OpenAlex

This paper is concerned with the performance investigation of single-phase grid-connected inverters in which rotating reference frame control strategy is used. Single-phase grid-connected inverters have found wide applications in interfacing small renewable energy sources to the grid. Naturally the single phase structure of these systems prevents using some well-known three-phase control strategies. One method to overcome this limitation is to virtually generate an orthogonal quantity and then exploit the existing three-phase control methods. This approach requires a phase-shifting block to generate the additional control signal. In this paper, several methods used to accomplish this task are investigated and compared from the standpoints of dynamic and steady-state performance. Therefore, the complete state-space model of the system is derived and linearized for small-signal studies. The results of comparison between different approaches are given.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.377
Teacher spread0.282 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

Explore more

Same topicMicrogrid Control and OptimizationFrench-language works237,207