MétaCan
Menu
Back to cohort
Record W2019683160 · doi:10.1109/tsg.2014.2303857

Towards Smart Integration of Distributed Energy Resources Using Distributed Network Protocol Over Ethernet

2014· article· en· W2019683160 on OpenAlexaff
Esteban Padilla, Kodjo Agbossou, Alben Cardenas

Bibliographic record

VenueIEEE Transactions on Smart Grid · 2014
Typearticle
Languageen
FieldEngineering
TopicSmart Grid Security and Resilience
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsSmart gridComputer sciencePower-line communicationEthernetCommunications protocolEmbedded systemComputer networkDistributed computingDistributed generationNetwork architectureEngineeringPower (physics)

Abstract

fetched live from OpenAlex

The Distributed Network Protocol (DNP3) is the communication protocol standardized by IEEE for electric power systems (EPS) and it is a promising communication standard in the context of Smart Grids. This paper presents the Distributed Network Protocol over Ethernet (E-DNP3) using field programmable gate array (FPGA) technology as the first step to the EPS automation. The communication architecture improves the delivery time comparing with previous works and provides an adaptable solution for real-time information exchange in electrical applications such as management, control, and protection of power systems. The developed work allows the analysis of the feasibility of the proposed communication architecture in a real microgrid scenario. This implementation accomplishes a new technique for the communication architecture in order to integrate the distributed energy resources (DERs) to the local power system. The experimental results prove that the proposed architecture satisfies the communication requirements for real-time monitoring and control of EPS towards Smart Grid applications.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.240
Teacher spread0.226 · 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
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

Citations35
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueIEEE Transactions on Smart GridSame topicSmart Grid Security and ResilienceFrench-language works237,207