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Record W1995257826 · doi:10.1109/pes.2010.5589720

Multi-microgrid control systems (MMCS)

2010· article· en· W1995257826 on OpenAlexaff
Edward J Ng, Ramadan El‐Shatshat

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrogridIslandingSmart gridDistributed computingComputer scienceAnticipation (artificial intelligence)GridReliability (semiconductor)Distributed generationPower (physics)Control engineeringControl (management)Reliability engineeringEngineeringElectrical engineeringRenewable energyArtificial intelligence

Abstract

fetched live from OpenAlex

The objective of this paper is to present a new concept related to the revitalized microgrid concept and the paradigm of the smart grid. A combination of a rapidly aging North American power infrastructure, a clear trend towards distributed generation, and an emphasis on electrical reliability has spurred a shift toward a more distributed, decentralized power grid. The concept of the microgrid has dominated the discussion of power distribution on a localized level, while the smart grid model has been touted as the new macrogrid design paradigm. In order to bring together the microgrid and smart grid concepts, we propose the MMCS concept, which divide distribution systems into numerous microgrid-like regions, or MMCS regions, to facilitate the smart grid concepts of self-healing, intentional islanding, and anticipation for the entire distribution network while inheriting the dynamics of a microgrid. We implement the MMCS concept through the use of multi-agent systems.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.003
GPT teacher head0.171
Teacher spread0.167 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations84
Published2010
Admission routes1
Has abstractyes

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