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Record W3094093640 · doi:10.1109/access.2020.3032325

A New Structure for PMG-Based WECSs With Battery Storage Systems

2020· article· en· W3094093640 on OpenAlexafffund
S. A. Saleh, X. F. St. Onge

Bibliographic record

VenueIEEE Access · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer science

Abstract

fetched live from OpenAlex

This paper develops and tests the performance of a new structure for grid-connected permanent magnet generator (PMG)-based wind energy conversion systems (WECSs) that have battery storage units (BSUs). The new structure is called the split dc-bus, and it is designed to reduce the pulsations in PMG developed torque and fluctuations in the power delivered to the grid. The reduction in PMG torque pulsations is achieved by employing a 5-level ac-dc power electronic converter (PEC) as the generator-PEC, whose dc outputs are processed by a two-port active dc-link. This active dc-link charges the BSUs and supplies the discharging PEC. The reduction of fluctuations in the delivered power is achieved by discharging the BSUs at point-of-common-coupling (PCC). The generator-side PEC, dc-link, grid-side PEC, and discharging PEC, are operated by a supervisor droop controller. The split-dc bus PMG-based WECS is implemented for performance testing under different wind speeds and levels of power delivery. Test results demonstrate reductions in PMG torque pulsations, along with reductions in the power delivered to the grid. These features of the split-dc bus PMG-based WECS are found to be complimented with a minor sensitivity to the changes in wind speed and changes in the power delivered to the grid.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.205
Teacher spread0.192 · 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 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

Citations17
Published2020
Admission routes2
Has abstractyes

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