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Record W4400032705 · doi:10.1109/tii.2024.3409445

Unsymmetrical Per-Phase Control for Reactive Power-Sharing Enhancement in Unbalanced Islanded Microgrids

2024· article· en· W4400032705 on OpenAlexaff
Dalia Yousri, Hany E. Z. Farag, Hatem Zeineldin, Ahmed Al‐Durra, Ehab F. El‐Saadany

Bibliographic record

VenueIEEE Transactions on Industrial Informatics · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of WaterlooYork University
FundersAdvanced Technology Research Council
KeywordsVoltage droopMicrogridDistributed generationAC powerControl reconfigurationBenchmark (surveying)Control theory (sociology)EngineeringElectrical impedancePower (physics)Computer scienceVoltageControl (management)Voltage regulatorRenewable energyElectrical engineeringEmbedded system

Abstract

fetched live from OpenAlex

Ensuring the cost-effective operation of an unbalanced islanded microgrid (UBIMG) hinges on achieving a proportional power sharing relative to the capacity of the connected distributed energy resource units (DERs). However, inherent characteristics of UBIMG, such as heterogeneous line impedance and unbalanced loads, inevitably result in mismatching the reactive power-sharing (RPS) among the droop-controlled DERs. As a solution, this article introduces an advanced control scheme that combines unsymmetrical per-phase droop control with unsymmetrical per-phase virtual impedance, referred to as unsymmetrical per-phase droop-virtual impedance control (USPDVIC), to enhance the RPS among DERs within the UBIMG. To determine the settings of the proposed control scheme, this study formulates a multiobjective optimization approach to minimize the average generation costs and mismatching in the per-phase RPS within the UBIMG across a set of operating states simultaneously. The performance of the proposed USPDVIC is comprehensively evaluated within a parallel architecture UBIMG and a radial UBIMG-based IEEE 13-bus, IEEE 34-bus, and IEEE 123-bus benchmark systems under various states of operation. These states include changes in loading conditions, plug-and-play of DERs, and system reconfiguration and partitioning. The results, along with comparisons to existing literature, provide solid evidence for the effectiveness of the proposed control scheme in improving the per-phase RPS among the parallel-connected and dispersed DERs within UBIMGs.

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 score0.978
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.021
GPT teacher head0.261
Teacher spread0.240 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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