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Duality Principle Enabled Systematic Analysis and Operation Design of New Multiport Converters for Renewable Generation Integration

2022· article· en· W4312546582 on OpenAlexaff
Pasan Gunawardena, Yuzhuo Li, Yunwei Li

Bibliographic record

Venue2022 IEEE 13th International Symposium on Power Electronics for Distributed Generation Systems (PEDG) · 2022
Typearticle
Languageen
FieldEngineering
TopicMultilevel Inverters and Converters
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDuality (order theory)ConvertersMicrogridTopology (electrical circuits)Computer scienceNetwork topologyRenewable energyTransformation (genetics)Power (physics)Mathematical optimizationMathematicsEngineeringElectrical engineeringPhysics

Abstract

fetched live from OpenAlex

Multiport converters (MPC) have been extensively studied for renewable energy and microgrid applications in recent years. However, the derivations of different types of MPCs are originated from the practical experience and the engineering viewpoint of the designer. In this article, a systematic approach to derive the MPC topologies based on the duality theory is proposed. Even though the duality theory has been used in power electronic converter topology, modulation, and control strategy derivations for decades, it is seldomly implemented in MPC research. In this article, an existing MPC topology is selected and a novel MPC is derived based on the proposed duality transformation process. The presented case study verifies the applicability of this work in practical MPC applications. The challenges, practical concerns, future directions, and advantages of the proposed framework are also summarized for comparison.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
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 head0.028
GPT teacher head0.259
Teacher spread0.231 · 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 designBench or experimental
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

Citations6
Published2022
Admission routes1
Has abstractyes

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