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Record W2077684077 · doi:10.1109/epec.2013.6802962

Open-loop maximum power point tracking strategy for Marine Current Turbines based on resource prediction

2013· article· en· W2077684077 on OpenAlexaff
Francisco Paz, Martin Ordonez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsControl theory (sociology)Maximum power point trackingComputer scienceOperating pointMaximum power principlePredictabilityVariable (mathematics)EngineeringVoltageElectronic engineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This paper presents the theory for a model-based Maximum Power Point Tracking (MPPTs) strategy for Marine Current Turbines (MCTs) that computes the optimal operating point using resource prediction and a look-up table. The high predictability of the Marine Currents (MCs) is exploited in order to produce a simple algorithm that calculates offline the optimal operating point for an evolving date and time. This eliminates the use of sensors to measure the current or the use of a perturbation to scan the curve. Two MPPT methods based on this strategy are discussed in detail: 1) fixed rotating speed with variable pitch angle and 2) fixed pitch angle with variable rotating speed. As a result, the two proposed MPPT methods improve the efficiency of the MCT creating a sensor-less, open loop strategy with precision and simplicity. The information needed to predict the MC is obtained through site characterization, a process required for any investment in green energy. The effectiveness of the proposed strategy is demonstrated through simulations.

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: none
Teacher disagreement score0.727
Threshold uncertainty score0.999

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)0.0020.000

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 head0.029
GPT teacher head0.261
Teacher spread0.231 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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