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Record W4378965585 · doi:10.18280/jesa.560214

Finite Control Set MPC for Voltage and Frequency Stabilization in Islanded AC Microgrids with Line Impedance Consideration and Enhanced Power Sharing Control

2023· article· en· W4378965585 on OpenAlexvenueno aff
Mohamed Islam Grairia, Riad Toufouti

Bibliographic record

VenueJournal Européen des Systèmes Automatisés · 2023
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsnot available
Fundersnot available
KeywordsControl theory (sociology)Electrical impedanceAC powerAutomatic frequency controlVoltagePower (physics)Control (management)Line (geometry)Model predictive controlComputer scienceEngineeringMathematicsElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents a hierarchical wireless control scheme for parallel distributed generation (DG) units in islanded AC microgrids.The proposed control scheme is divided into primary and secondary control layers.The primary control layer includes an improved finite set-model predictive control (FS-MPC) with a dualobjective cost function based on the proposed droop approach.The benefit of the proposed droop approach is the ability to generate the reference voltage without any amplitude deviation, unlike the conventional droop technique, which may cause deviations.Furthermore, double virtual impedances are proposed, with the first one being used to reduce the effects of feeders' mismatched impedance, while the second one is used to increase the output impedance resistance, leading to enhanced accuracy of active and reactive power sharing among DG units.The secondary control layer helps to establish global controllability by keeping the frequency and amplitude voltage of the AC bus at their nominal values.The simulation results show that the proposed control is capable of ensuring appropriate power sharing among parallel DG units and achieving voltage quality at the AC bus under different load 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 score0.316
Threshold uncertainty score0.876

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 head0.010
GPT teacher head0.226
Teacher spread0.215 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueJournal Européen des Systèmes AutomatisésSame topicMicrogrid Control and OptimizationFrench-language works237,207