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Record W2491180951 · doi:10.1109/pedg.2016.7527103

Three-phase reactive power control using one-cycle controller for wind energy conversion systems

2016· article· en· W2491180951 on OpenAlexaff
Snehal Bagawade, Majid Pahlevani, Shangzhi Pan, Praveen Jain

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsQueen's University
Fundersnot available
KeywordsAC powerPower factorControl theory (sociology)Volt-ampere reactiveVoltage optimisationVoltage regulationWind powerPower controlVoltageGenerator (circuit theory)Switched-mode power supplyPower (physics)Permanent magnet synchronous generatorComputer scienceEngineeringElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This paper presents a new power factor correction scheme for a three phase wind energy conversion systems (WECSs). The proposed scheme provides power factor correction (PFC) with respect to the back-EMF of a permanent magnet synchronous generator (PMSG) instead of the terminal voltage. Since there exists a series impedance between the back-EMF and terminal voltage, performing PFC with respect to the terminal voltage leads to the additional reactive power generated by the back EMF. This additional reactive power degrades the capacity of the generator. In this paper, a novel three-phase reactive power control technique based on the one-cycle control scheme is presented in order to eliminate the need for additional reactive power. In the proposed technique, the terminal voltage and current of each phase are measured and the optimal power factor is regulated at the generator's terminals. The optimal reactive power is determined in the proposed control scheme by introducing a fictitious reactive current, derived from the input voltage, in the control law. The simulation and experimental results show the optimal performance of the proposed technique in terms of reactive power.

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 score0.989
Threshold uncertainty score0.526

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 head0.009
GPT teacher head0.206
Teacher spread0.197 · 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

Citations3
Published2016
Admission routes1
Has abstractyes

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