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Record W2142467829 · doi:10.1109/ecce.2009.5316196

Effects of nonlinear efficiency characteristics on the power-tracking control: a case study of hydrokinetic energy conversion system

2009· article· en· W2142467829 on OpenAlexaff
Mohammad Junaid Khan, M. Tariq Iqbal, John E. Quaicoe

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMemorial University of NewfoundlandPowertech Labs (Canada)
Fundersnot available
KeywordsControl theory (sociology)Nonlinear systemCascadeMaximum power point trackingEnergy transformationOperating pointPower (physics)Efficient energy useEnergy conversion efficiencyComputer scienceEnergy (signal processing)Process (computing)Maximum power principleElectricity generationTracking (education)EngineeringControl (management)Electronic engineeringMathematicsPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Maximum power point tracking (MPPT) for many alternative energy conversion systems implies the application of control methods where the operation of the primary energy conversion process is optimized through a nonlinear control arrangement. This assumes the presence of constant efficiency values for the subsystems in cascade to the front-end process. In case, efficiency of the subsequent stages are not constant and are dependent on diverse operating conditions, it becomes important to identify the success of power tracking as seen by the load unit. In this work hydrokinetic energy conversion systems are studied in this regard. A repetitive approach that matches nonlinear efficiency information to the overall performance of the system is presented. With specific focus on `power curve' and `performance/efficiency curve' two figures of merit are introduced to identify issues such as success of power tracking and divergence from optimum operating point. A comprehensive simulation study and a experimental test example are also presented. This method can also be used for identifying the effects on efficiency nonlinearity in other alternative energy systems.

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: Empirical
Teacher disagreement score0.314
Threshold uncertainty score0.360

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 head0.003
GPT teacher head0.171
Teacher spread0.168 · 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

Citations2
Published2009
Admission routes1
Has abstractyes

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