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Record W3147436946 · doi:10.1109/tec.2021.3068758

Analysis of Feasible Synchronverter Pole-Placement Region to Facilitate Parameter Tuning

2021· article· en· W3147436946 on OpenAlexaff
Shuan Dong, Christine Chen

Bibliographic record

VenueIEEE Transactions on Energy Conversion · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsControl theory (sociology)Realization (probability)Computer scienceClosed-loop poleController (irrigation)Electric power systemFull state feedbackPower (physics)Mode (computer interface)Control engineeringMathematicsEngineeringControl (management)Physics

Abstract

fetched live from OpenAlex

This paper derives in analytical closed form the feasible pole-placement region of the synchronverter active-power loop (APL) so as to eliminate all trial-and-error effort in parameter tuning. We consider the well-established setting of a reduced third-order APL model with two controller parameters that can be tuned freely. Thus, only two of the three APL poles can be specified independently in hopes of achieving desired system dynamics. Central to the presented derivation is the realization that the two specified poles must represent the dominant mode of the system. Otherwise the actual system dynamics may be dictated by the third unspecified pole, leading to unexpected or undesired dynamic behaviour. Numerical simulations involving the full-order synchronverter dynamical model and a modified New England 39-bus test system validate the analysis and the resulting region within which poles must be placed for actual system dynamics to match desired ones.

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)0.002
metaresearch head score (Gemma)0.008
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.017
GPT teacher head0.196
Teacher spread0.179 · 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

Citations10
Published2021
Admission routes1
Has abstractyes

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