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Record W2031452860 · doi:10.3390/app2040801

Power Management System for Load Banks Supplied by Pitch Controlled Wind Turbine System

2012· article· en· W2031452860 on OpenAlexafffund
Adel Merabet, Raquel Keeble, Vigneshwaran Rajasekaran, Rachid Beguenane, Hussein Ibrahim, J. S. Thongam

Bibliographic record

VenueApplied Sciences · 2012
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsCentre Intégré de Santé et Services Sociaux de la GaspésieRoyal Military College of CanadaSaint Mary's University
FundersFaculty of Graduate Studies and Research, University of Alberta
KeywordsTurbineSupervisorPower management systemPower (physics)Electric power systemAutomotive engineeringComputer scienceEngineeringElectrical engineeringControl theory (sociology)Power managementControl (management)Mechanical engineeringPhysics

Abstract

fetched live from OpenAlex

An automatic power management system, to monitor the distribution of power to a set of load banks, is presented in this paper. The required power is generated from a laboratory-size pitch-controlled wind turbine experimental workstation. The management system is a sequence of logic expressions, based on the generated power, a profile of the banks’ states provided by the supervisor and the banks’ priorities, to obtain an optimal behavior of the system and to ensure the load requirement. A modified PI pitch angle control is proposed to regulate the generated power for tracking the power reference in order to maintain a consistent brilliance of the load’s LEDs, to reduce the activity of the pitch actuator and to deal with fluctuation problems. Experimental results are provided to show the effectiveness of the proposed automatic power management system for load banks supplied by a pitch controlled wind turbine.

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 score0.005
Threshold uncertainty score0.015

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)0.0050.001

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 head0.006
GPT teacher head0.198
Teacher spread0.191 · 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

Citations5
Published2012
Admission routes2
Has abstractyes

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