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Record W4210294373 · doi:10.1109/tia.2022.3146117

HVDC Transmission and its Potential Application in Remote Communities: Current Practice and Future Trend

2022· article· en· W4210294373 on OpenAlexafffund
Xiaodong Liang, Mehdi Abbasipour

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2022
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHigh-voltage direct currentRenewable energyTransmission systemTransmission (telecommunications)Electric power systemElectric power transmissionPower transmissionElectrical engineeringComputer scienceTelecommunicationsAsynchronous communicationEngineeringPower (physics)VoltageDirect currentPhysics

Abstract

fetched live from OpenAlex

High voltage direct current (HVdc) transmission systems play an essential role in our modern power grids, not only providing bulk power transmission and asynchronous ac connection, but also enabling renewable energy integration into power grids. Renewable energy integration through HVdc transmission systems leads to the creation of multiterminal HVdc (MT-HVdc) grids. Recently, the idea of electrifying remote communities by HVdc transmission systems has emerged, and multi-terminal small-scale HVdc grids have been investigated in such applications. Despite numerous applications and significant potential of HVdc transmission systems, various technical and economic challenges in utilizing these systems still persist. In this article, an extensive literature review is conducted on HVdc transmission systems, particularly focusing on their role in renewable energy integration, and their potential application in remote communities. Future research directions are also recommended.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0010.001
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 head0.013
GPT teacher head0.259
Teacher spread0.246 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations70
Published2022
Admission routes2
Has abstractyes

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