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Record W3091969658 · doi:10.1109/jestpe.2020.3028586

Electric Spring Using MPUC5 Inverter for Mitigating Harmonics and Voltage Fluctuations

2020· article· en· W3091969658 on OpenAlexaff
Amirabbas Kaymanesh, Ambrish Chandra

Bibliographic record

VenueIEEE Journal of Emerging and Selected Topics in Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
Fundersnot available
KeywordsHarmonicsTotal harmonic distortionInverterControl theory (sociology)VoltageWaveformController (irrigation)MicrogridPower (physics)EngineeringElectronic engineeringComputer scienceElectrical engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

This article introduces a novel configuration of electric spring (ES) based on the modified five-level packed u-cell (MPUC5) inverter for mitigating harmonics and voltage fluctuations at various points of a grid with unstable generated power from distributed renewable energy sources. Immanent merits of the proposed configuration include, but are not limited to, halved dc-links voltages, boost mode operation, the possibility of higher power applications, smooth five-level voltage waveform with low total harmonic distortion (THD) index, the smaller size of output low-pass filter, and low switching frequency. The operation principles, design procedure, and configuration of the MPUC5-based ES (MPUC5-ES) are also presented. Besides, a simple and yet efficient controller without any extra control loop for regulating dc bus voltages has been proposed. Finally, the introduced multilevel ES is tested through extensive simulation and experimental studies to confirm its dynamic and steady-state performances in various operation modes in a weak grid fed by both conventional and intermittent renewable energy sources.

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 science0.0010.000
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 head0.010
GPT teacher head0.221
Teacher spread0.210 · 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

Citations36
Published2020
Admission routes1
Has abstractyes

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