MétaCan
Menu
Back to cohort
Record W4206037113 · doi:10.1109/ias48185.2021.9677046

Microgrid Formation and Service Restoration in Distribution Systems: a Review

2021· review· en· W4206037113 on OpenAlexaff
Xiaodong Liang, Md Abu Saaklayen, Mosayeb Afshari Igder, Shah Mohammad Rezwanul Haque Shawon, S.O. Faried

Bibliographic record

Venue2021 IEEE Industry Applications Society Annual Meeting (IAS) · 2021
Typereview
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMicrogridRenewable energyElectric power systemComputer scienceReliability engineeringReliability (semiconductor)Distributed generationIT service continuitySingle point of failureInterconnectionService (business)Systems engineeringDistributed computingEngineeringPower (physics)TelecommunicationsElectrical engineeringComputer networkBusiness

Abstract

fetched live from OpenAlex

Due to increasing penetration of renewable energy sources, microgrids with the self-adequacy feature becomes a promising platform in distribution systems to interconnect geographically closed distributed generation (DG) units and load, which enables faster service restoration during disturbances and natural disasters, and offers improved system reliability. In this paper, an extensive literature review is conducted for optimal planning of microgrids during system planning stage, and microgrid formation during system operation stage in active distribution systems to achieve improved system performance and enhanced service restoration. In addition, the soft open point as an emerging power electronic device in distribution systems by connecting feeders or networked microgrids is reviewed, which can be used in system restoration. The future research directions in these important areas are recommended in the paper.

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)0.000
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: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.020
GPT teacher head0.271
Teacher spread0.252 · 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
GenreReview

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

Citations7
Published2021
Admission routes1
Has abstractyes

Explore more

Same venue2021 IEEE Industry Applications Society Annual Meeting (IAS)Same topicMicrogrid Control and OptimizationFrench-language works237,207