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Record W2462979812 · doi:10.1109/cjece.2016.2520664

Offshore Wind Farm Power Control Using HVdc Link

2016· article· en· W2462979812 on OpenAlexvenueno aff
Navid Ghardash khani, Mehrdad Abedi, Gevork B. Gharehpetian, Gholam Hosein Riahy

Bibliographic record

VenueCanadian Journal of Electrical and Computer Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsnot available
Fundersnot available
KeywordsOffshore wind powerWind powerMarine engineeringConvertersGridPower (physics)EngineeringAC powerPower controlWind speedControl theory (sociology)Power optimizerElectric power systemVoltageControl (management)Automotive engineeringElectrical engineeringComputer scienceMaximum power point trackingMeteorologyInverterPhysicsMathematics

Abstract

fetched live from OpenAlex

In this paper, a method is presented to control offshore wind farm output power. This method is able to fix the wind farm output power even during wind speed variations. In the proposed method, the offshore wind farm is connected to the onshore grid through the high-voltage dc (HVdc) cable. Moreover, the power control of the wind turbines is achieved by controlling the HVdc convertors. In the proposed system, the generator side convertors have to control the active power absorbed from the wind, and the grid side ones are obtained to control the HVdc link voltage. The control system is based on applying the appropriate modulation index to the voltage source converters. Two control strategies are proposed and analyzed to control wind farm output power. The simulation results illustrate that the proposed method is able to smooth the output power of the offshore wind farms appropriately. The proposed wind farm configuration and the control system are validated by simulations in theMATLAB/Simulink environment.

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 score0.002
Threshold uncertainty score0.006

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 communication0.0000.000
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 head0.004
GPT teacher head0.154
Teacher spread0.150 · 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

Citations12
Published2016
Admission routes1
Has abstractyes

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