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Record W2394518441 · doi:10.1109/apec.2016.7468028

A series-DG based autonomous islanding microgrid

2016· article· en· W2394518441 on OpenAlexaff
Beihua Liang, Yunwei Li, Jinwei He, Chengshan Wang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIslandingMicrogridVoltage droopConvertersDistributed generationAC powerComputer scienceControl theory (sociology)Power (physics)VoltageSeries (stratigraphy)Electronic engineeringEngineeringVoltage sourceElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

A series distributed generation (DG) units based islanding microgrid configuration and the corresponding power sharing method are proposed in this paper. Unlike a conventional islanding microgrid where parallel DG units are connected to the point of common coupling (PCC) to supply electricity to loads in a cooperative manner, this paper discusses the possibility of using series connected low voltage (LV) converters as an alternative solution. It has been demonstrated that the DC/DC boost conversion in the back stage of a conventional DG unit can be removed from this proposed microgrid. In addition, via a very simple power factor-frequency inverse droop control, an accurate real and reactive power sharing can be simultaneously achieved in multiple series DG units. Note that the effectiveness of the proposed power sharing control does not rely on the communications between DG units or the knowledge of detailed microgird circuitry parameters. Therefore, the challenging power sharing problems in conventional islanding microgrids is perfectly solved with this proposed configuration and the control method.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.812

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)0.0010.000

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 head0.003
GPT teacher head0.154
Teacher spread0.151 · 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 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

Citations1
Published2016
Admission routes1
Has abstractyes

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