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Record W2547423647 · doi:10.1109/ccece.2016.7726736

Analysis of a micro-grid behavior by a supervisory control and data acquisition system-experimental validation

2016· article· en· W2547423647 on OpenAlexaffabout
R. Debibi, Hussein Ibrahim, K. Belmokhtar, Adrian Ilinca, Daniel R. Rousse, Ambrish Chandra, Drishtysingh Ramdenee, Anis Ben Arfi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsCegep de Sept IlesUniversity of CalgaryÉcole de Technologie SupérieureCentre Intégré de Santé et Services Sociaux de la GaspésieUniversité du Québec à Rimouski
Fundersnot available
KeywordsSCADAMicrogridSupervisory controlSoftware deploymentGridComputer scienceEnergy management systemData acquisitionReliability engineeringControl systemEmbedded systemControl (management)Real-time computingEnergy managementDistributed computingEngineeringEnergy (signal processing)Operating systemElectrical engineering

Abstract

fetched live from OpenAlex

The aim of this paper is to present the benefits of the use of Supervisory Control And Data Acquisition (SCADA) system for the deployment of a micro-grid supplied by hybrid energy system in remote sites and unconnected grids. A microgrid, based in the experimental test site of TechnoCentre éolien (TCE) at Riviere-au-Renard (Québec), and equipped by a SCADA system has been selected as case study for this paper. An overview on the SCADA communication system of micro-grids is presented and several validations by using the SCADA system of the TCE's microgrid are studied and analyzed. Indeed, the SCADA system used in, allows studying different behavior of the TCE's microgrid and it offers effective methods for improving the control systems, equipment performance and the management of remote areas.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.565
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.011
GPT teacher head0.217
Teacher spread0.207 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations0
Published2016
Admission routes2
Has abstractyes

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