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Using Dynamic Phasors to Model a Single-Phase Active Rectifier Based on Lyapunov Current Control

2022· article· en· W4310929542 on OpenAlexafffund
Udoka C. Nwaneto, Andrew M. Knight

Bibliographic record

VenueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics Society · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates
KeywordsControl theory (sociology)Total harmonic distortionLyapunov functionFeed forwardRectifier (neural networks)Three-phaseConvertersComputer sciencePhasorHarmonicNonlinear systemEngineeringPower (physics)Electric power systemControl engineeringVoltageControl (management)Physics

Abstract

fetched live from OpenAlex

To ensure low harmonic pollution in power systems, the restrictions on total harmonic distortion (THD) produced by AC/DC converters are being made more stringent by utilities. Single-phase active rectifiers are among the power electronic conditioning devices used to ensure that loads meet the utility THD limits. Linear control schemes based on proportional-integral (PI) controllers are commonly used in single-phase active rectifiers due to ease of tuning. However, linear controllers do not provide global asymptotic stability. Lyapunov-based nonlinear control strategy enables converters to have global asymptotic stability. However, most models of Lyapunov-based single-phase active rectifiers in the literature appear in a detailed form. Detailed models require relatively large computational effort to give accurate results. In this paper, a single-phase active rectifier based on Lyapunov inner current and load current feedforward control schemes is modeled with dynamic phasors (DPs). The Lyapunov function is derived from the most dominant harmonic in each state variable. Simulation results show that the DP model is about 220 times faster than a detailed switching model. There is a close match between the DP and detailed model results. The proposed DP model is useful for the fast-paced study of multi-active-rectifier-converter systems.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.059
GPT teacher head0.294
Teacher spread0.235 · 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.

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

Citations0
Published2022
Admission routes2
Has abstractyes

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Same venueIECON 2022 – 48th Annual Conference of the IEEE Industrial Electronics SocietySame topicAdvanced DC-DC ConvertersFrench-language works237,207