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Record W4289133143 · doi:10.3390/en15155567

Multi-Pulse Rectifier Based on an Optimal Pulse Doubling Technique

2022· article· en· W4289133143 on OpenAlexaff
Rohollah Abdollahi, Gevork B. Gharehpetian, Fazel Mohammadi, Saravana Prakash P

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsTotal harmonic distortionRectifier (neural networks)HarmonicsPulse-width modulationComputer scienceElectronic engineeringDiodePower (physics)Pulse (music)Power factorControl theory (sociology)EngineeringElectrical engineeringDetectorVoltagePhysicsArtificial neural network

Abstract

fetched live from OpenAlex

This paper presents pulse multiplication technology based on an optimal Pulse Doubling Technique (PDT) to upgrade a 28-Pulse Rectifier (28-PR) to a 56-PR. The optimal PDT comprises a Tapped Interphase Reactor (TIPR) with a low kVA-rating and two diodes. The number of pulses can be increased from 28 to 56 using the PDT so that the input current harmonics are reasonably mitigated. Additionally, the 14-phase Polygon-Connected Autotransformer (PCA) is designed in such a way that it can be used for retrofit applications. A detailed simulation analysis in the MATLAB/Simulink environment is carried out, and the results show that the improved quality indices of the final AC input and DC output power are equivalent to the IEEE 519-2014 standard and meet sensitive industrial application requirements with an input current Total Harmonic Distortion (THD) lower than 3%. Moreover, the power factor also maintained unity for a wide operating range. The optimal PDT scheme is affordable and easy to implement as only a small-capacity PDT (only 1% of the output power) is needed to double the pulse number. An experimental prototype is developed to verify the simulation results.

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 categoriesInsufficient payload (model declined to judge)
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 score0.212
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0010.000

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 head0.020
GPT teacher head0.236
Teacher spread0.215 · 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

Citations12
Published2022
Admission routes1
Has abstractyes

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