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Record W2759418042 · doi:10.1109/tsg.2017.2756935

Sliding-Mode Control of AC Voltages and Currents of Dispatchable Distributed Energy Resources in Master-Slave-Organized Inverter-Based Microgrids

2017· article· en· W2759418042 on OpenAlexafffund
Mohammad B. Delghavi, Amirnaser Yazdani

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2017
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaRyerson University
KeywordsDispatchable generationMicrogridControl theory (sociology)VoltageMaster/slaveInverterEngineeringSliding mode controlDistributed generationPower (physics)Control (management)Computer scienceElectrical engineeringRenewable energyPhysicsNonlinear system

Abstract

fetched live from OpenAlex

This paper proposes a current-controlled voltage-mode control scheme for dispatchable electronically coupled distributed energy resource (DER) units based on sliding-mode control (SMC) strategy. The proposed control strategy provides fast and stable control on the terminal voltage and frequency of DER unit and ensures protection of the power-electronic interface to external faults. In addition it maintains the quality of the output voltage of the host DER unit in spite of unbalanced and/or distorted load currents. Performance of the proposed SMC strategy is demonstrated through time-domain simulation of a single islanded DER unit, starting from black-start and subjected to various operating scenarios, and it is compared to the performance of a proportional-integral (PI) based control strategy. Also a current-mode control strategy is proposed for DER units based on SMC. The performance of both voltage- and current-mode control strategies are demonstrated in a sample master-slave organized three-unit microgrid.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.002

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)0.0010.000

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 head0.008
GPT teacher head0.203
Teacher spread0.195 · 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

Citations109
Published2017
Admission routes2
Has abstractyes

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