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Record W2148427295 · doi:10.1109/pes.2007.385442

Impact of Interface Controls on the Steady-State Stability of Inverter-Based Distributed Generators

2007· article· en· W2148427295 on OpenAlexaff
Xiaoyu Wang, Walmir Freitas, Wilsun Xu, Venkata Dinavahi

Bibliographic record

VenueIEEE Power Engineering Society General Meeting · 2007
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIslandingInverterControl theory (sociology)Steady state (chemistry)Controller (irrigation)Power (physics)AC powerMaximum power transfer theoremConstant (computer programming)Computer scienceConstant currentDistributed generationElectric power systemAutomatic frequency controlEngineeringCurrent (fluid)Control (management)PhysicsElectrical engineeringTelecommunicationsChemistry

Abstract

fetched live from OpenAlex

This paper presents a study about the impact of different inverter interface controls on the steady-state stability of inverter-based distributed generators (DG) in the presence of positive feedback anti-islanding schemes. The impact of the constant power controller and the constant current controller on the DG system steady-state stability is compared. The Sandia frequency shift (SFS) anti-islanding control is included in the inverter controllers. The comparison results show that in grid parallel mode the constant current-controlled DG can transfer more power to the connected power system when the local load level is low. However, the constant power-controlled DG has higher power transfer capability for heavy load situations.

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)0.001
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.234
Teacher spread0.224 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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