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Record W3123017477 · doi:10.1109/sta50679.2020.9329351

An efficient PV-Energy storage DC Microgrid utilization scheme

2020· article· en· W3123017477 on OpenAlexaff
Rim Ben Salah, Jamel Ghouili

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsMicrogridScheme (mathematics)Energy storageComputer scienceEnvironmental sciencePhotovoltaic systemElectrical engineeringRenewable energyEngineeringPower (physics)Physics

Abstract

fetched live from OpenAlex

The proliferation of Renewable Energy Resources (DER's) into Direct Current (DC) microgrids, growing rapidly, have attracted great consideration of the researchers providing numerous challenges, compared to AC microgrids. With the coupling technology for DER”s., the main benefit of power electronics is the capability to provide effective control in order to deal with voltage variations related to renewable sources intermittency and load disturbances. In this paper, a non-linear method based on sliding mode control (SMC) is implemented in order to control the bidirectional converter of the storage system component and ensure the stable operation of the MG. In fact, voltage regulation and system stability are crucial tasks in order to ensure the stable operation of islanded DC microgrid. The effectiveness and robustness pertaining to the proposed controller compared to proportional integral (PI) controller are validated through MATLAB/Simulink simulation under sudden variations of power sources and load. Lastly, results show superiority and demonstrate that the proposed method has a good performance in reducing and eliminating voltage oscillations and voltage ripples that jeopardize the system stability. Bus voltage stabilizing and power quality enhancement are realized and achieved.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.950
Threshold uncertainty score0.457

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 head0.013
GPT teacher head0.196
Teacher spread0.184 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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