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Record W3012544813 · doi:10.1049/iet-est.2019.0145

Hierarchical control architecture for resilient interconnected microgrids for mass transit systems

2020· article· en· W3012544813 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueIET Electrical Systems in Transportation · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsGridEngineeringControl (management)ArchitectureSmart gridTransport engineeringComputer scienceDistributed computingElectrical engineering

Abstract

fetched live from OpenAlex

Railway transit is relied on every day to transport millions of passengers and bring billions worth of economic goods to market. However, electrified railway infrastructures are dependent on the electric grid, which is vulnerable to extreme weather, changing supply and demand patterns, and cyber‐terrorism. A hierarchical control system that uses a resiliency metric and applies game theory techniques to handle the exchange of energy between interconnected microgrids is presented. The proposed designs are modelled and simulated in Simulink, for a proposed high‐speed railway in the UK. Interconnected microgrids for railway infrastructures demonstrate a reduced dependence on the electric grid by at least 95%. The results are both extremely impressive and promising towards a more resilient and stable energy future both for railway and for other critical infrastructures.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.976
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.192
Teacher spread0.186 · 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