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Record W3206518973 · doi:10.15676/ijeei.2021.13.3.4

Operation Mode Transition of a Low-Voltage Single Phase Microgrid based on Synchronization Controller

2021· article· en· W3206518973 on OpenAlexaff
Qusay Salem, Khaled Alzaareer, Salman Harasis

Bibliographic record

VenueInternational Journal on Electrical Engineering and Informatics · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsMicrogridSynchronization (alternating current)Controller (irrigation)Mode (computer interface)Phase synchronizationVoltageControl theory (sociology)Computer scienceElectrical engineeringMaterials scienceEngineeringControl (management)Topology (electrical circuits)Artificial intelligence

Abstract

fetched live from OpenAlex

In case of system contingencies, improving the continuity of the electrical supply by islanding capabilities is of great interest.Besides, reconnecting the power network after an islanding event is also an important issue for maintaining the power system stability.This paper investigates the disconnection and reconnection of a microgrid consisting of two DG voltage source inverters in case of a grid disturbance.The main problem is to achieve a smooth transition between both modes of operation by implementing proper control strategies for the DGs and incorporating a robust synchronization controller.The synchronization controller is implemented to synchronize the microgrid with the main grid after an unintentional islanding condition.Flip flops are utilized to manage the set and reset operation of the static switch and the DG VSI operation mode depending on the network status.The system performance under the short circuit condition has been validated through simulations using SimPowerSystems toolbox in Matlab/Simulink software.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.009

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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.003
GPT teacher head0.195
Teacher spread0.193 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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