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Record W4408610800 · doi:10.1109/tsg.2025.3552770

Improved Control and Stability Analysis of a Microgrid Connector Controller Under Unbalanced Network Conditions

2025· article· en· W4408610800 on OpenAlexafffund
Hanwen Gu, Behnam Tamimi, Claudio A. Cañizares

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsMicrogridCable glandControl theory (sociology)Controller (irrigation)Stability (learning theory)EngineeringControl engineeringControl (management)Computer scienceElectrical engineeringVoltage

Abstract

fetched live from OpenAlex

The microgrid connector controller has been shown to be a more cost-effective alternative for a microgrid-grid interface than a back-to-back connector. This paper proposes an improved control system for this controller under unbalanced operation, as distribution feeder and microgrid loads are normally unbalanced. The controller regulates the utility interface while minimizing the impact of load unbalancing on both the utility feeder and the microgrid. An improved reference current generation strategy is designed to suppress the fluctuations of the dc link voltage, thereby preventing them from being introduced into the control system and affecting the controller operation. Furthermore, a four-stage start-up strategy is proposed to avoid an external dc source for charging the controller’s dc link capacitor, making the interface more practical and cost-effective. The paper also presents a comprehensive investigation of the effects of the controller parameters and microgrid load unbalance on the small-perturbation stability of grid-connected microgrids. The performance of the presented controller is compared with its original controls, a back-to-back with existing unbalance control techniques, and a switch interface through detailed simulations in a benchmark test system. The results illustrate that the proposed controls can minimize the impact of feeder and microgrid unbalancing by eliminating the negative-sequence components and reducing the fluctuations in the transferred powers and dc link voltage, showing similar overall performance to a back-to-back interface.

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 score0.919
Threshold uncertainty score0.888

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.005
GPT teacher head0.202
Teacher spread0.198 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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