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Dynamic Behavior and Management Strategy of Hybrid Wind/Fuel Cell System

2013· article· en· W2050015269 on OpenAlexvenueno aff
A. Meharrar, Mustapha Hatti, M. Tioursi, Tahar Benmessaoud

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
Fundersnot available
KeywordsWind powerHybrid powerMaximum power point trackingAutomotive engineeringEnergy managementWind speedInterfacingPower managementHybrid systemEngineeringMATLABRenewable energyPower (physics)Computer scienceControl theory (sociology)Control engineeringEnergy (signal processing)Electrical engineeringControl (management)Inverter

Abstract

fetched live from OpenAlex

hybrid generation system is considered as a solution for the uncontrolled energy production from such dispersed sources as wind generation. In this paper, modeling and control of wind/FC system is proposed. Dynamics models for the main system components, namely, wind energy conversion system (WECS), fuel cell, electrolyses, power electronic interfacing circuits, hydrogen storage tank and ultra-capacitor are developed. Also, a variable speed wind generation maximum power point tracking (MPPT) based on Adaptative Neuro-Fuzzy Inference system (ANFIS) is presented. Based on the dynamic component model, a simulation model for the proposed hybrid energy system has been developed using Matlab/Simulink and the power flows management strategy are proposed. The result shows that this system can tolerate the rapid changes in wind speed and/or power demand. This results shows also that, the overall power management strategy is effective and the power flows among the different energy sources and the load demand is balanced successfully.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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.

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

Citations0
Published2013
Admission routes1
Has abstractyes

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