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Record W2903150140 · doi:10.1109/ias.2018.8544640

Implementation of a new Control for Hybrid Wind-Diesel for Water Treatment Standalone System

2018· article· en· W2903150140 on OpenAlexaff
F. Dubuisson, Miloud Rezkallah, Ambrish Chandra, Maarouf Saad, M. Tremblay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsSuez (Canada)École de Technologie Supérieure
Fundersnot available
KeywordsMaximum power point trackingVoltage droopDiesel generatorMicrogridAutomotive engineeringBuck converterWind speedPhotovoltaic systemBattery (electricity)Wind powerControl theory (sociology)Boost converterComputer sciencePower (physics)VoltageDiesel fuelEngineeringElectrical engineeringVoltage sourceInverterControl (management)

Abstract

fetched live from OpenAlex

This paper deals with the control of a diesel generator and a voltage source converter in a standalone microgrid for water treatment application. Droop control is used to ensure the power sharing and ensure regulation of the AC voltage and frequency at the Point of Common Coupling (PCC). A Perturb and Observe (P&O) method based on integral action is used to achieve the Maximum Power Point Tracking (MPPT) without using speed sensors from the variable speed Wind Energy Conversion System (WECS). A Battery Energy Storage System (BESS) is connected to the DC bus through a DC-DC buck-boost converter to ensure power leveling during wind and load variation. The performance of the proposed system are tested using Matlab/Simulink under load and weather variation.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

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 communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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 designSimulation or modeling
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

Citations2
Published2018
Admission routes1
Has abstractyes

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