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Record W2526981415 · doi:10.1109/tsg.2015.2479576

Frequency Control in Isolated/Islanded Microgrids Through Voltage Regulation

2015· article· en· W2526981415 on OpenAlexafffund
Mostafa Farrokhabadi, Claudio A. Cañizares, Kankar Bhattacharya

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2015
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaABB
KeywordsMicrogridControl theory (sociology)Automatic frequency controlVoltage regulationController (irrigation)Sensitivity (control systems)VoltageVoltage controllerEngineeringControl engineeringVoltage regulatorComputer scienceVoltage droopControl (management)Electronic engineeringElectrical engineering

Abstract

fetched live from OpenAlex

This paper presents a frequency control mechanism for an isolated/islanded microgrid through voltage regulation. The proposed scheme makes use of the load voltage sensitivity to operating voltages and can be easily adopted for various types of isolated microgrids. The proposed controller offers various advantages, such as allowing the integration of significant levels of intermittent renewable resources in isolated/islanded microgrids without the need for large energy storage systems, providing fast and smooth frequency regulation with no steady-state error, regardless of the generator control mechanism. The controller requires no extra communication infrastructure, and only local voltage and frequency is used as feedback. The performance of the controller is evaluated and validated through various simulation studies in the PSCAD/EMTDC software environment based on a realistic microgrid test system, using small-perturbation stability analysis to demonstrate the positive effect of the proposed controller in system damping.

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)0.001
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.011
GPT teacher head0.203
Teacher spread0.191 · 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

Citations170
Published2015
Admission routes2
Has abstractyes

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