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A Comprehensive Review on Microgrid Architectures for Distributed Generation

2019· review· en· W3018352928 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

Venuenot available
Typereview
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsMicrogridDistributed generationPhotovoltaic systemReliability (semiconductor)GridComputer scienceWind powerDistributed computingReliability engineeringElectricity generationRenewable energyEngineeringElectrical engineeringPower (physics)

Abstract

fetched live from OpenAlex

In the recent years, there has been a growing interest in the concept of microgrids to integrate distributed generation systems like solar photovoltaic (PV) and wind to reduce greenhouse gas (GHG) emissions, to provide higher reliability for critical loads and to supply electricity in areas not served by existing grid infrastructure. A microgrid (MG) is a portion of the electrical system which views generation and associated loads as a subsystem, with the ability to operate both grid connected or islanded from grid, thus maintaining a high level of service and reliability. The existing grid infrastructure, the distributed energy resources to be integrated, as well as specific customer-oriented requirements will determine the best fitting architecture to constitute a microgrid. In this review, most common microgrids architectures based on AC, DC and hybrid AC/DC buses are analyzed. Furthermore, comparisons are made between different microgrid architectures. Positive and negative features of different architectures are given as a guide for further microgrid system studies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
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 head0.056
GPT teacher head0.297
Teacher spread0.241 · 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

Quick stats

Citations24
Published2019
Admission routes1
Has abstractyes

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