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Record W2130868072 · doi:10.1109/tsg.2010.2095046

Power Management of Inverter Interfaced Autonomous Microgrid Based on Virtual Frequency-Voltage Frame

2011· article· en· W2130868072 on OpenAlexaff
Yan Li, Yunwei Li

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicrogridVoltage droopAC powerVoltage optimisationPower controlEngineeringAutomatic frequency controlControl theory (sociology)Volt-ampere reactiveVoltageIslandingPower factorElectric power systemVoltage regulationPower managementPower (physics)Computer scienceInverterElectronic engineeringVoltage regulatorElectrical engineeringDistributed generationControl (management)

Abstract

fetched live from OpenAlex

This paper presents the power management scheme for a power electronics based low voltage microgrid in islanding operation. The proposed real and reactive power control is based on the virtual frequency and voltage frame, which can effectively decouple the real and reactive power flows and improve the system transient and stability performance. Detailed analysis of the virtual frame operation range is presented, and a control strategy to guarantee that the microgrid can be operated within the predetermined voltage and frequency variation limits is also proposed. Moreover, a reactive power control with adaptive voltage droop method is proposed, which automatically updates the maximum reactive power limit of a DG unit based on its current rating and actual real power output and features enlarged power output range and further improved system stability. Both simulation and experimental results are provided in this paper.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0000.000
Open science0.0010.000
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 head0.009
GPT teacher head0.182
Teacher spread0.172 · 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

Citations364
Published2011
Admission routes1
Has abstractyes

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