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Record W3041991098 · doi:10.3390/en13143554

A Selective Fault Clearing Scheme for a Hybrid VSC-LCC Multi-Terminal HVdc System

2020· article· en· W3041991098 on OpenAlexafffund
Naushath M. Haleem, Athula Rajapakse, A.M. Gole, Ioni Fernando

Bibliographic record

VenueEnergies · 2020
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsManitoba HydroUniversity of Manitoba
FundersManitoba Hydro
KeywordsFault (geology)EngineeringRectifier (neural networks)Terminal (telecommunication)Electrical engineeringVoltageCircuit breakerVoltage sourceTransmission lineLine (geometry)Control theory (sociology)Electronic engineeringComputer scienceTelecommunicationsMathematics

Abstract

fetched live from OpenAlex

A selective fault clearing scheme is proposed for a hybrid voltage source converter (VSC)-line commutated converter (LCC) multi-terminal high voltage direct current (HVdc) transmission structure in which two small capacity VSC stations tap into the main transmission line of a high capacity LCC-HVdc link. The use of dc circuit breakers (dc CBs) on the branches connecting to VSCs at the tapping points is explored to minimize the impact of tapping on the reliability of the main LCC link. This arrangement allows clearing of temporary faults on the main LCC line as usual by force retardation of the LCC rectifier. The faults on the branches connecting to VSC stations can be cleared by blocking insulated gate bipolar transistors (IGBTs) and opening ac circuit breakers (ac CB), without affecting the main line’s performance. A local voltage and current measurement based fault discrimination scheme is developed to identify the faulted sections and pole(s), and trigger appropriate fault recovery functions. This fault discrimination scheme is capable of detecting and discriminating short circuits and high resistances faults in any branch well before 2 ms. For the test grid considered, 6 kA, 2 ms dc CBs can easily facilitate the intended fault clearing functions and maintain the power transfer through healthy pole during single-pole faults.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.268
Threshold uncertainty score0.702

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 head0.021
GPT teacher head0.224
Teacher spread0.203 · 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

Citations1
Published2020
Admission routes2
Has abstractyes

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