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Primary Frequency Control in Islanded Microgrids Using a Novel Smart Load

2020· article· en· W3107216555 on OpenAlexaff
Javad Khodabakhsh, Ebrahim Mohammadi, Gerry Moschopoulos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsMicrogridAutomatic frequency controlConvertersComputer scienceRenewable energyInertiaEnergy storageSmart gridElectric power systemDistributed generationControl theory (sociology)EngineeringTopology (electrical circuits)Control engineeringPower (physics)Control (management)Electrical engineeringTelecommunicationsVoltage

Abstract

fetched live from OpenAlex

Islanded microgrids are an inexpensive way to supply electricity to consumers in remote areas. They consist of synchronous diesel generators, distributed generators (DGs) that use renewable energy resources, energy storage systems (ESSs), and loads. The low inertia of the DGs; however, interfaces increase the complexity of frequency control in islanded microgrids and increases the possibility of system instability. In this paper, a new frequency control framework that is based on using single-stage AC-DC converters as virtual synchronous machine (VSM) smart loads to emulate the behavior of a synchronous machine (SM) is proposed to increase system inertia and reduce frequency oscillations. The AC-DC converter is based on a new topology that has been proposed by the authors. In this paper, the topology and the control system are explained and an IEEE 37 bus test feeder with the smart loads is simulated as an islanded microgrid. It is shown that using the proposed smart load results in frequency fluctuations being damped considerably faster.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.858
Threshold uncertainty score0.544

Codex and Gemma teacher scores by category

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.178
Teacher spread0.167 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

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