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Record W2785864832 · doi:10.1109/epec.2017.8286211

A new modified maximum power extraction technique for wind and hybrid renewable energy systems

2017· article· en· W2785864832 on OpenAlexaff
Kajanan Kanathipan, John Lam

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnergy
TopicPhotovoltaic System Optimization Techniques
Canadian institutionsYork University
Fundersnot available
KeywordsWind powerDuty cycleWind speedController (irrigation)Renewable energyMaximum power principleComputer scienceControl theory (sociology)Maximum power point trackingSolar energyPower optimizerAutomotive engineeringEnvironmental sciencePhotovoltaic systemEngineeringMeteorologyElectrical engineeringPhysicsVoltageControl (management)

Abstract

fetched live from OpenAlex

The power extracted from solar and wind energy systems vary with the change of the weather, reducing the efficiency of the system. As a result, an energy efficient method is required to locate the optimal operating point to extract the maximal amount of energy under different atmospheric conditions. This paper proposed a modified perturb and observe maximum power point tracking controller for wind and solar-wind hybrid energy systems. The controller uses a modified fixed perturb and observe method to reach MPP by varying the duty cycle of each input module. Results are provided on a 200W wind system as well as a 400W solar-wind energy system with varying irradiation intensity and wind speed to highlight the merits of the work.

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: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.948
Threshold uncertainty score0.942

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 head0.020
GPT teacher head0.265
Teacher spread0.245 · 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
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

Citations10
Published2017
Admission routes1
Has abstractyes

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