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Record W2775637800 · doi:10.1109/iecon.2017.8216196

Fault calculations in AC-DC hybrid systems

2017· article· en· W2775637800 on OpenAlex
Shahram Negari, Dewei Xu

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

VenueIECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society · 2017
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceFault (geology)ConvertersHybrid systemGridRenewable energyFault detection and isolationPower (physics)Smart gridDistributed generationElectrical engineeringElectronic engineeringEngineeringVoltagePhysics

Abstract

fetched live from OpenAlex

Hybrid AC-DC systems are expected to flourish rapidly as part of the transition from conventional grid to smart grid. Effective detection, classification, and mitigation of faults in hybrid systems require accurate modelling of the entire system as a whole, and its components as well. Moreover, precise computation of fault current is an indispensable part of any fault management scheme. Such modelling frameworks have long been existing for either three-phase AC, or DC grids. However, existing algorithms turn to be inadequate in calculating the fault current characteristics in full AC-DC hybrid systems that include distributed generation sources and energy storage units. This paper reviews the complexities involved in determining fault current characteristics in such hybrid AC-DC where switch-mode power converters interface AC and DC and renewable resources are integrated in the DC subsystem.

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)0.001
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: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.874

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.267
Teacher spread0.209 · 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