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Record W2088121962 · doi:10.1109/ecce.2012.6342551

Multi-mode control for photovoltaic grid-connected interleaved flyback micro-inverters to achieve high efficiency in wide load range

2012· article· en· W2088121962 on OpenAlexaff
Zhiliang Zhang, Xiaofei He, Xiaoyong Ren, Xin Li, Yan‐Fei Liu

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsQueen's University
Fundersnot available
KeywordsFlyback transformerSolar micro-inverterInverterPhotovoltaic systemFlyback converterControl theory (sociology)Flyback diodeRange (aeronautics)Computer scienceElectronic engineeringControl (management)VoltageMaterials scienceMaximum power point trackingEngineeringElectrical engineeringBoost converterTransformer

Abstract

fetched live from OpenAlex

Boundary Conduction Mode (BCM) and Discontinuous Conduction Mode (DCM) control strategies are widely used for the flyback micro-inverter. BCM and DCM control strategies are investigated for the interleaved flyback micro-inverter concentrating on the loss analysis under different load condition. These two control strategies have different impact on the loss distribution and thus the efficiency of the flyback micro-inverter. Based on the loss analysis, a new hybrid control strategy combing the two-phase DCM and one-phase DCM control is proposed to improve the efficiency in wide load range by reducing the dominant losses depending on the load current. The experimental results verified the benefits 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 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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.633
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.015
GPT teacher head0.262
Teacher spread0.247 · 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.

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

Citations6
Published2012
Admission routes1
Has abstractyes

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