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Record W4224236861 · doi:10.18280/rces.090102

Research on Pre-Synchronization Control Strategy of Optical Storage VSG Off-Grid Switching

2022· article· en· W4224236861 on OpenAlexvenueno aff
Yufei Peng, Hua Li, Xiaoyan Li, Jikang Wang, Xinyu Zhang

Bibliographic record

VenueReview of Computer Engineering Studies · 2022
Typearticle
Languageen
FieldEnergy
TopicPower Systems and Renewable Energy
Canadian institutionsnot available
Fundersnot available
KeywordsMicrogridSynchronizingComputer scienceInrush currentGridSynchronization (alternating current)InverterFrequency gridControl theory (sociology)VoltageElectronic engineeringControl (management)EngineeringTransformerElectrical engineeringTelecommunications

Abstract

fetched live from OpenAlex

In the optical storage DC microgrid in island mode, in view of the large inrush current problem of the inverter controlled by the optical storage VSG when switching from off-grid to grid-connected operation mode, a pre-synchronization control strategy based on secondary frequency regulation and voltage regulation was proposed. This strategy can adjust the large frequency and voltage fluctuations caused by active and reactive load changes under the independent DC microgrid of the optical storage. At the same time, it can complete the effective tracking of the grid phase before connecting to the grid, and reduce the impulse current when the optical storage microgrid is isolated and connected to the grid. The real-time digital simulation platform (RTDS) is used to simulate and analyze the proposed control scheme, and the simulation results verify the feasibility of the proposed control strategy.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.006

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 communication0.0010.000
Open science0.0010.000
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 head0.029
GPT teacher head0.315
Teacher spread0.286 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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