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Record W2588900156 · doi:10.1063/1.4976141

PMSG based wind system for real-time maximum power generation and low voltage ride through

2017· article· en· W2588900156 on OpenAlexaff
Muhammed Y. Worku, M. A. Abido, Reza Iravani

Bibliographic record

VenueJournal of Renewable and Sustainable Energy · 2017
Typearticle
Languageen
FieldEngineering
TopicWind Turbine Control Systems
Canadian institutionsUniversity of Toronto
FundersKing Fahd University of Petroleum and Minerals
KeywordsLow voltage ride throughPermanent magnet synchronous generatorControl theory (sociology)Maximum power point trackingFault (geology)Wind powerController (irrigation)EngineeringVoltageTurbineGridConvertersMaximum power principlePower (physics)Computer scienceAC powerElectrical engineeringControl (management)

Abstract

fetched live from OpenAlex

This work presents an efficient control strategy for low voltage ride through (LVRT) and maximum power tracking of a permanent magnet synchronous generator (PMSG) based variable speed grid connected wind turbine generator. Three-level Neutral Point Clamped converters are connected back to back to perform the power conversion. A controller is proposed and implemented for the machine side converter to maximize the power generation and loss minimization under varying wind speed. A controller is also proposed and implemented for the grid side converter to transfer the generated power to a local load and grid by keeping the DC link voltage constant. To test the LVRT capability, a three phase short circuit fault is applied on the grid side. The real time digital simulator results on a 2 MW/4 kV PMSG verify the effectiveness and superiority of the proposed control scheme under different loading and fault conditions.

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 score0.007

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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