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Record W3018327636 · doi:10.1109/tpel.2020.2989197

Modular Interline DC Power Flow Controller

2020· article· en· W3018327636 on OpenAlexaff
Zhong Xu, Miao Zhu, Yunwei Li, Shuai Wang, Han Wang, Xu Cai

Bibliographic record

VenueIEEE Transactions on Power Electronics · 2020
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Alberta
FundersNational Key Research and Development Program of China Stem Cell and Translational ResearchScience and Technology Project of State Grid
KeywordsModular designController (irrigation)Power (physics)Power flowLine (geometry)AC powerPower controlControl theory (sociology)GridComputer scienceTopology (electrical circuits)EngineeringFlow (mathematics)Electronic engineeringControl engineeringElectrical engineeringElectric power systemControl (management)VoltagePhysicsMathematics

Abstract

fetched live from OpenAlex

DC power flow controller (DCPFC) is regarded as an effective technology to improve the active power distribution capability in a complex dc grid. Among different types of DCPFCs, the interline dc power flow controller (IDCPFC) can realize multiline power flow control functions in a complex dc grid. In this article, a modular multiline IDCPFC has been proposed via a transformerless structure. Specifically, the n-line IDCPFC can actively control (n-1)-line power flow based on the theoretical analysis. The proposed technology is analyzed in detail, including topology, operation principle, and control strategy. As a case study, a three-line IDCPFC is implemented in this article. Both simulation and experimental results are obtained to show that the proposed IDCPFC can effectively control multiline power flows under various conditions.

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 score0.006
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.196
Teacher spread0.189 · 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

Citations31
Published2020
Admission routes1
Has abstractyes

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