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Modulated Model Predictive Torque and Current Control of Squirrel Cage Induction Generator-Based Wind Power Generation System

2020· article· en· W3108391173 on OpenAlexafffund
Venkata Yaramasu, Apparao Dekka, José Rodríguez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsLakehead University
FundersLakehead University
KeywordsControl theory (sociology)Model predictive controlTorqueDirect torque controlVector controlDuty cycleInduction generatorWind powerStatorAC powerEngineeringSquirrel-cage rotorComputer scienceVoltageInduction motorControl (management)Electrical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper investigates novel control methods for the squirrel-cage induction generator based variable speed wind power generation system composed of back-to-back connected two-level voltage source converters. A modulated model predictive torque control method is designed by combining the operating principles of direct torque control, predictive torque control and space vector modulation to effectively control the electromagnetic torque and stator flux control of an induction generator. Similarly, a modulated model predictive current control method is designed for the grid-side converter to control the active and reactive powers injected to the three-phase grid. The proposed control methods employ duty-cycle optimized cost functions for fixed switching frequency operation, fast dynamic response and low steady-state errors during a wide-dynamic range operation. Simulation results are presented with a 750-kW wind power generation system to validate the effectiveness of proposed control methods.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score0.553

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 head0.022
GPT teacher head0.198
Teacher spread0.176 · 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

Citations5
Published2020
Admission routes2
Has abstractyes

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