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Record W2908678785 · doi:10.1109/epec.2018.8598349

On the Existence of Voltage Collapse in Islanded Microgrid

2018· article· en· W2908678785 on OpenAlexaff
A. A. Eajal, Ameen Hassan Yazdavar, Ehab F. El‐Saadany, K. Ponnambalam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
FundersKhalifa University of Science, Technology and ResearchUtah Agricultural Experiment Station
KeywordsMicrogridIslandingVoltageDistributed generationAC powerConstant (computer programming)Power (physics)Smart gridVoltage regulationComputer scienceControl theory (sociology)EngineeringElectrical engineeringRenewable energyPhysicsControl (management)

Abstract

fetched live from OpenAlex

The future smart grid entails clusters with plug-and-play features known as microgrids. Each microgrid hosts a mix of distributed energy resources including synchronous-based. Nevertheless, synchronous-based generators, are characterised by their limited reactive power capabilities which could lead to voltage collapse problem during islanding. Microgrids also comprises controllable loads. The majority of modern loads are power-electronic-interfaced and demand voltage regulation at their ends, exhibiting constant power characteristics. Microgrids with high penetrations of constant-power loads are vulnerable to voltage collapse especially during contingencies such as weather-caused outages. To this end, this paper investigates the possibility voltage collapse phenomenon in islanded microgrids during contingencies. The voltage stability analysis was carried out on an islanded 6- bus microgrid. Several case studies were designed in order to reveal the likelihood of voltage collapse in microgrids under extreme events.

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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.006
GPT teacher head0.185
Teacher spread0.178 · 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

Citations2
Published2018
Admission routes1
Has abstractyes

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