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Record W2768003534 · doi:10.1109/ias.2017.8101755

Hardware testing of sliding mode controller for improved performance of VSC-HVDC based offshore wind farm under DC fault

2017· article· en· W2768003534 on OpenAlexaff
Mounir Benadja, Miloud Rezkallah, Seghir Benhalima, Ab. Hamadi, Ambrish Chandra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsOffshore wind powerConvertersControl theory (sociology)Voltage sourceHigh-voltage direct currentTransformerEngineeringController (irrigation)Wind powerDirect currentComputer scienceVoltageElectrical engineering

Abstract

fetched live from OpenAlex

This paper proposes a simulation and validation of a sliding mode control (SMC) for the onshore voltage-source-converter based high-voltage-direct-current (VSC-HVDC) transmission system. The onshore VSC-HVDC station is based on a two-level voltage topology and used for the interconnection between the offshore wind farm (OWF) and the ac main grid via two submarines dc cables. The OWF is composed of ten variable speed wind turbines based on permanent magnet synchronous generators (VSWT/PMSGs). The VSWT/PMSGs are connected in parallel to dc-bus. The ac main grid receives the required active power from the OWF through transformer, ac-dc diode bridge rectifiers, boost converters, two DC cables and VSC-HVDC station. The boost converters are used to force the machines to operate at speeds for maximum power extraction from the VSWT. SMC is applied to VSC-HVDC station to ensure better stability during dc fault and avoiding saturation while using linear controller and parameters adjustment. The effectiveness of the proposed system operation under dc fault is demonstrated by simulations carried out using Matlab/Simulink. Also, a scaled-down prototype of the system is built and tested in laboratory to validate the performance of the proposed control scheme.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.676
Threshold uncertainty score0.552

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 head0.030
GPT teacher head0.255
Teacher spread0.225 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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