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Record W4385319080 · doi:10.3390/en16155611

A Distributed Control Scheme for Cyber-Physical DC Microgrid Systems

2023· article· en· W4385319080 on OpenAlexaff
A. K. Onaolapo, Gulshan Sharma, Pitshou N. Bokoro, Anuoluwapo Aluko, Giovanni Pau

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Calgary
FundersUniversity of Johannesburg
KeywordsMicrogridVoltage droopDistributed generationRobustness (evolution)Control theory (sociology)Controller (irrigation)Computer scienceVoltageScheme (mathematics)EngineeringControl (management)Voltage sourceElectrical engineering

Abstract

fetched live from OpenAlex

An innovative distributed secondary control technique for balanced current sharing and voltage regulation for an off-grid DC microgrid setup is presented in this research. The droop control scheme is conventionally used for current sharing amongst distributed sources (DSs) in a microgrid. However, this method has two major drawbacks. Firstly, due to the line resistance of each DS, the output voltage is different for each DS, and the output current-sharing property deteriorates. Secondly, the droop action increases the DC bus voltage variation. To address these drawbacks, a fuzzy-based distributed secondary controller is proposed. The proposed controller in each DS simultaneously ensures balanced current sharing and sustains DC bus voltage at the reference value by using a communication network to interact with one another. The required circumstance to guarantee the proposed controller’s stability is provided. The stability analysis is beneficial to inform the choice of control parameters. The real-time simulation outputs demonstrate the proposed control scheme’s robustness in achieving the control objectives under varying operating conditions.

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: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.492

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 head0.006
GPT teacher head0.199
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 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

Citations5
Published2023
Admission routes1
Has abstractyes

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