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Record W4382982862 · doi:10.3390/en16135085

Monitoring Technologies for HVDC Transmission Lines

2023· article· en· W4382982862 on OpenAlexafffund
Jeff Laninga, Ali Nasr Esfahani, Gevindu Ediriweera, Nathan D. Jacob, Behzad Kordi

Bibliographic record

VenueEnergies · 2023
Typearticle
Languageen
FieldEngineering
TopicEnergy Harvesting in Wireless Networks
Canadian institutionsUniversity of ManitobaManitoba Hydro
FundersManitoba Hydro
KeywordsElectric power transmissionHVACTransmission (telecommunications)Power transmissionElectrical engineeringEngineeringTransmission systemComputer scienceElectric power systemTelecommunicationsPower (physics)Mechanical engineeringAir conditioning

Abstract

fetched live from OpenAlex

HVDC transmission systems are becoming more prevalent because of the advantages they offer. They are more efficient and environmentally friendly and are becoming preferred in distributed power generation. The reliable operation of HVDC transmission lines requires distributed, online monitoring, which is not as well-developed as those in an HVAC system. The advancement of HVDC systems will require online monitoring that provides information on the operating and environmental conditions, mechanical stress on the conductors and the structure, vegetation clearance, and security of the system. This perspective paper provides a thorough overview of the state-of-the-art technologies that are applicable to the monitoring of HVDC transmission lines. The challenges and future direction in the development of sensors for HVDC applications are highlighted. One of the key challenges unique to HVDC transmission lines is energy harvesting from the transmission line conductors to provide power for the monitoring equipment. This paper reviews the potential technologies for energy harvesting from HVAC transmission lines and their suitability for employment in HVDC transmission lines.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score0.532

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 head0.018
GPT teacher head0.241
Teacher spread0.223 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations24
Published2023
Admission routes2
Has abstractyes

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