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Record W2944432655 · doi:10.1109/pedes.2018.8707596

A Novel Optimal Space Vector Modulation Technique of Current Source Inverter for Solar Power Integration

2018· article· en· W2944432655 on OpenAlexaff
Akshay Kumar Rathore, Amarendra Edpuganti, Dipti Srinivasan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsConcordia University
Fundersnot available
KeywordsTotal harmonic distortionPulse-width modulationSpace vector modulationComputer scienceInverterModulation (music)Electronic engineeringDistortion (music)Power (physics)Control theory (sociology)HarmonicEngineeringTelecommunicationsBandwidth (computing)PhysicsArtificial intelligenceAcousticsControl (management)

Abstract

fetched live from OpenAlex

The goal of the paper is to develop a novel modulation technique that combines merits of space vector modulation (SVM) scheme and synchronous optimal pulse-width modulation (SOPWM) technique for current source inverter (CSI). SVM provides better dynamic performance but unable to produce less total harmonic distortion (THD) on inverter current at low device switching frequency (<; kHz). SOPWM provides less THD even at low device switching frequency however it needs a conversion technique to operate CSI and it needs an additional control technique to improve dynamic performance. Hence to overcome these limitations, this paper develops a single modulation technique called optimal SVM technique that can achieve less THD as well as better dynamic performance. The proposed method do not require an additional conversion technique to operate CSI and it provides less THD even while operating under low device switching frequency of 450 Hz. Experimental results prove that the proposed modulation technique can provide better steady state performance as well as dynamic performance.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.958
Threshold uncertainty score0.472

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 head0.248
Teacher spread0.226 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations7
Published2018
Admission routes1
Has abstractyes

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