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Record W2142314152 · doi:10.1109/iecon.2013.6699170

An adaptive nonlinear MPPT controller for stand alone marine current energy conversion systems

2013· article· en· W2142314152 on OpenAlexaff
Nahidul Khan, S. F. Rabbi, M.J. Hinchey, M.A. Rahman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsControl theory (sociology)Maximum power point trackingBacksteppingMaximum power principleController (irrigation)Permanent magnet synchronous generatorNonlinear systemComputer sciencePower (physics)VoltageAdaptive controlControl engineeringEngineeringInverterControl (management)

Abstract

fetched live from OpenAlex

This paper presents an online estimation based adaptive nonlinear maximum power point tracking (MPPT) controller for a stand-alone permanent magnet synchronous generator (PMSG) based marine current energy conversion system. The proposed control strategy requires no flow sensor and also does not need the parameters of a PMSG. A nonlinear control algorithm for the load side converter to extract maximum power has been proposed to adequately deal with the inherent nonlinearities in the energy conversion system. A Lyapunov based online estimation approach is used to continually estimate the time varying input voltage and the output load resistance of the converter. Detailed simulation results of the proposed nonlinear controller namely adaptive backstepping are presented and fully analyzed. Simulation results demonstrate that the proposed nonlinear controller can incessantly extract maximum power from the ocean current at various flow speeds.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.893
Threshold uncertainty score0.899

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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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 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
Published2013
Admission routes1
Has abstractyes

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