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Record W4401990106 · doi:10.1109/access.2024.3451520

Seamless Transition Between Microgrid Operation Modes Using ADRC Without an Islanding Detection Algorithm nor PLL

2024· article· en· W4401990106 on OpenAlexafffund
Naïm Yalaoui, Louis‐A. Dessaint, Mohammad Reza Dehbozorgi, Kamal Al‐Haddad

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsIslandingMicrogridPhase-locked loopComputer scienceAlgorithmControl theory (sociology)Electronic engineeringElectrical engineeringEngineeringTelecommunicationsArtificial intelligenceDistributed generationControl (management)

Abstract

fetched live from OpenAlex

The availability and cost of fossil fuels, natural disasters, aging infrastructure, climate change, and rising electricity consumption have affected today’s power grids. One of the most practical solutions for achieving green and reliable energy is the use of microgrids. The stability of microgrids dominated by electronic converters presents several challenges. Among the problems encountered are the absence of physical inertia, delay in detecting islanding, and loss of stability associated with the transition between operating modes and variations of the load power. To overcome these challenges, this study presents a new robust control strategy based on active disturbance rejection control (ADRC). It is suitable for both islanded and connected operation modes with a single control, without an islanding detection algorithm or Phase-Locked Loop (PLL). The effectiveness of the control strategy is demonstrated through simulations and a comparative analysis with conventional droop control. Flexibility of the transition is also ensured. The proposed control strategy is successfully validated using a TI C2000 DSP TMS320F28335 microcontroller.

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 score0.612
Threshold uncertainty score0.781

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 communication0.0010.002
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 head0.027
GPT teacher head0.288
Teacher spread0.261 · 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

Citations7
Published2024
Admission routes2
Has abstractyes

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