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Record W2020774111 · doi:10.1109/ccece.2006.277806

A High Performance Wind-Electric Battery Charging System

2006· article· en· W2020774111 on OpenAlexaff
Suzan Eren, Jiaxue Hui, Dinh Du To, D. Yazdani

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsQueen's University
Fundersnot available
KeywordsWind powerAutomotive engineeringCharge controllerBattery (electricity)Computer scienceController (irrigation)Electrical engineeringRectifier (neural networks)Energy storageState of chargePower (physics)Engineering

Abstract

fetched live from OpenAlex

Wind power can offer an economic and environmentally friendly alternative to conventional methods of power supply, and is especially suitable for remote off-grid locations. This paper focuses on the design of a wind energy conversion system that consists of a rectifier, a dc-dc converter, and a smart controller that maximizes the capture of wind energy, thereby minimizing the amount of wasted energy. The charge controller provides suitable charging conditions and regulates the current flow to avoid overcharge for battery protection. Different charging algorithms are analyzed and evaluated for its effectiveness in a wind energy conversion system. Simulation results will verify the feasibility of the energy conversion system.

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 categoriesInsufficient payload (model declined to judge)
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 score0.470
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.005
GPT teacher head0.171
Teacher spread0.166 · 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

Citations11
Published2006
Admission routes1
Has abstractyes

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