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A Quantitative Analysis of Energy Storage Requirements for the Hybrid Cascaded Multilevel Converters

2020· article· en· W3108813863 on OpenAlex
Levi Bieber, Liwei Wang, Juri Jatskevich, Wei Li

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsOpal-Rt Technologies (Canada)Okanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
Fundersnot available
KeywordsConvertersComputer scienceEnergy storageElectronic engineeringReliability engineeringElectrical engineeringEngineeringPower (physics)VoltagePhysics

Abstract

fetched live from OpenAlex

With an ever-increasing need for reducing the converter size in voltage source converter high voltage direct current (VSC-HVDC) systems, special attention is put towards converters with a low energy storage requirement, as energy storage is a predictor of the overall converter size. The modular multilevel converter (MMC) technology has the benefit of efficient AC/DC conversion; however, for many cases, it re-quires a large converter footprint, due to the excessive quantity of energy storage (i.e., capacitors) needed. Recently, hybrid cascaded multilevel converters (HCMCs) have been proposed, which are a combination of the simpler two- and three-level converters and cascaded full-bridge sub-modules (FBSMs), and are expected to have a lower converter footprint, due to fewer sub-modules (SMs) than the MMC. However, the degree to which the HCMCs have an improved footprint has yet to be verified. This paper puts forth a method for determination of the total amount of energy that the hybrid two-level converter (H2LC) and the hybrid three-level converter (H3LC) require. The results show that both the H2LC and the H3LC have an improved footprint compared to the conventional MMC.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.211

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 head0.058
GPT teacher head0.274
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

Quick stats

Citations8
Published2020
Admission routes1
Has abstractyes

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