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Record W2949627523 · doi:10.1109/tia.2019.2923163

Solid-State Transformers for Distribution Systems–Part I: Technology and Construction

2019· article· en· W2949627523 on OpenAlexafffund
S. A. Saleh, Chistian Richard, X. F. St. Onge, K. McDonald, E. Ozkop, Liuchen Chang, Basim Alsayid

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2019
Typearticle
Languageen
FieldEngineering
TopicAdvanced DC-DC Converters
Canadian institutionsUniversity of New Brunswick
FundersTürkiye Bilimsel ve Teknolojik Araştırma KurumuUniversity of New Brunswick
KeywordsTransformerElectrical engineeringElectronic engineeringNetwork topologyVoltagePower engineeringAC powerEngineeringElectronic circuitCompensation (psychology)Low voltageComputer sciencePower factorComputer network

Abstract

fetched live from OpenAlex

Solid-state transformers (SSTs) are an emerging technology that has been developed to improve the stability, reliability, and economic operation of distribution systems. These new transformers are composed of a medium ac voltage (MV) stage, a dc stage, and a low ac voltage (LV) stage. Passive and active dc links are used to construct the dc stage in SSTs in order to offer new functionalities, including hybrid (ac and dc) distribution, reactive power compensation, voltage/frequency regulation, power quality improvement, and distributed generation utilization. On one hand, a distribution SST has its ac stage connected to an MV level, which mandates specific power electronic converter (PEC) topologies, switching element capabilities, and filtering circuits. On the other hand, the dc-link stage has to provide isolation between the MV and LV levels, which requires the employment of isolated dc PECs. Part I of this work provides a review of SST designs and constructions (for deployment in distribution systems), in terms of the required technology, supported functionalities, and construction features.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.005

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 head0.007
GPT teacher head0.239
Teacher spread0.232 · 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

Citations108
Published2019
Admission routes2
Has abstractyes

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