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Record W2906428308 · doi:10.1109/pesgm.2018.8586305

A Fast Self-synchronizing Synchronverter Design with Easily Tuneable Parameters

2018· article· en· W2906428308 on OpenAlexaff
Shuan Dong, Yu Christine Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSynchronizingComputer scienceControl theory (sociology)Controller (irrigation)Synchronization (alternating current)InductanceGridProcess (computing)VoltageEngineeringChannel (broadcasting)Electrical engineering

Abstract

fetched live from OpenAlex

This paper proposes a fast self-synchronizing synchronverter controller design, which synchronizes the synchronverter inner voltage to the grid-side voltage without needing to measure its phase angle, prior to physical connection to the grid. The proposed design centres on the addition of a virtual resistance branch (along with a suitable coordinate transformation), which provides the controller with virtual active- and reactive-power output feedback signals during self synchronization, even though the actual outputs are zero before grid connection. The virtual resistance branch enables fast self synchronization by avoiding inductance dynamics in prevailing methods that use a virtual impedance branch. At the same time, the parameter tuning process is simplified as fewer parameters require tuning. Moreover, the effects of these parameters on synchronization dynamics are well defined. Time-domain simulations are provided to validate that the proposed controller design self synchronizes quickly and that its parameters are easily tuneable.

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: Methods · Consensus signal: none
Teacher disagreement score0.782
Threshold uncertainty score0.509

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 head0.007
GPT teacher head0.161
Teacher spread0.154 · 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
GenreMethods

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

Citations9
Published2018
Admission routes1
Has abstractyes

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