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Multiple mode operation and control for VSG interfacing DC distribution system

2019· article· en· W3025495038 on OpenAlexaff
Hongzhou Luan, Xiaolin Zhang, Baoyi Wu, Chu Sun, Qiang Li, Yu Si, Xiaomei Wu, Dong Chen

Bibliographic record

Venue2019 4th IEEE Workshop on the Electronic Grid (eGRID) · 2019
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMcGill University
Fundersnot available
KeywordsVoltage droopInterfacingGridControl theory (sociology)Mode (computer interface)Oscillation (cell signaling)Low-frequency oscillationMicrogridComputer sciencePower (physics)Electronic engineeringEngineeringVoltageElectric power systemElectrical engineeringVoltage regulatorControl (management)Physics

Abstract

fetched live from OpenAlex

DC distribution system at low and medium voltage level is undergoing rapid development, driven by the growth of power electronic technology and integration of distributed generation. Virtual synchronous generator (VSG), which can operate in grid-connected mode and islanded mode, provides a convenient interconnecting interface between DC distribution system and AC grid. However, the multiple mode operation and transition of VSG in grid-connected and islanded process is not thoroughly studied, including start-up and reconnection. On the other hand, most research into oscillation mechanism in the past does not consider the outer frequency droop loop. To address these issues, this paper proposed to use VSG in DC distribution system. A phase and frequency regulator is designed to achieve smooth start-up and a method is proposed to realize reconnection of VSG with AC grid in the current research. Stability analysis considering the frequency droop loop is also conducted, based on which proper damping is selected to mitigate the oscillation in transition and grid-connected mode. The proposed strategy is verified by electro-magnetic type simulation on PSCAD/EMTDC.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.596
Threshold uncertainty score0.885

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)0.0000.000

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 head0.004
GPT teacher head0.190
Teacher spread0.186 · 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 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

Citations2
Published2019
Admission routes1
Has abstractyes

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