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Record W4413212556 · doi:10.1109/tnsm.2025.3594954

An Edge-Based Adaptive Event-Triggered Network Transmission Scheme for Fully Distributed Power and Frequency Control of Islanded AC Microgrids

2025· article· en· W4413212556 on OpenAlexaff
Sheng Han, Hong Zhu, Qishui Zhong, Kaibo Shi, Yankai Cao, Jianfeng Liu

Bibliographic record

VenueIEEE Transactions on Network and Service Management · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of British Columbia
FundersSichuan Association for Science and TechnologyNational Natural Science Foundation of China
KeywordsComputer scienceScheme (mathematics)Transmission (telecommunications)Automatic frequency controlPower controlEnhanced Data Rates for GSM EvolutionTransmission networkPower (physics)Electronic engineeringComputer networkDistributed computingTelecommunicationsEngineering

Abstract

fetched live from OpenAlex

In islanded AC microgrids, achieving optimal power distribution across distributed generators (DGs) and restoring frequency post-primary control under limited communication resources is paramount for ensuring stability and flexibility. In this paper, we develop a distributed adaptive secondary control strategy tailored for islanded AC microgrids. This strategy facilitates effective active power sharing and frequency regulation using solely local network information. A unique adaptive edge-based event-triggered transmission mechanism is also proposed, obviating Zeno behavior, to reduce communication overhead. It requires only the degree information of a single node in the DG network topology. Contrasting most existing research that necessitates global network topology details, like the Laplacian matrix, this work establishes a fully distributed control performance criterion that operates devoid of global data. The effectiveness of the proposed control approach is validated using a modified IEEE 34-bus test system.

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: Methods · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.984

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 head0.004
GPT teacher head0.198
Teacher spread0.194 · 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
GenreMethods

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

Citations2
Published2025
Admission routes1
Has abstractyes

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