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Record W2055909382 · doi:10.1109/ias.2014.6978403

Implementation of d-q decoupling and feed-forward current controller for grid connected three phase voltage source converter

2014· article· en· W2055909382 on OpenAlexaff
Azziddin M. Razali, Mohammad Azizur Rahman, Nasrudin Abd Rahim

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsDecoupling (probability)Voltage sourceControl theory (sociology)Power factorVoltageTotal harmonic distortionComputer scienceThree-phaseVoltage controllerController (irrigation)Current sourceAC powerEngineeringElectrical engineeringVoltage droopControl engineering

Abstract

fetched live from OpenAlex

This paper presents a vector current controller scheme, which operates according to grid virtual flux orientation for the three phase, 3-wire pulse-width modulated voltage source converter (VSC). The usage of voltage sensors to determine the grid voltage angle for synchronization and the grid voltage magnitude for the controller operation are avoided by applying a virtual flux concept in the proposed control scheme. The virtual flux concept is utilized to obtain the input power equations, which will be used to calculate the reference d (direct) and q (quadrature) axis currents. The estimated grid virtual flux provides a new approach in developing the proposed control structure of three phase VSC by including the d-q axes decoupling and the feed-forward components to enhance the performance of front-end VSC during load and supply voltage disturbances. Computer simulation and laboratory experiment are conducted to verify the operation and performances of the VSC under steady-state and transient conditions. The results indicate VSC utilizing the proposed virtual flux oriented control (VFOC) is able to produce unity power factor operation, low harmonic distortion of input line current, adjustable dc output voltage and fixed switching frequency.

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: none
Teacher disagreement score0.963
Threshold uncertainty score0.427

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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