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Record W2311439795 · doi:10.1049/cp.2015.0046

A Fast Calculation Method for the Local Commutation Failure Immunity Indices in Single- and Multi-infeed HVDC Systems

2015· article· en· W2311439795 on OpenAlex
Xiuyu Chen, Chunyi Guo, A.M. Gole

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

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of ManitobaElectrovaya (Canada)
Fundersnot available
KeywordsCommutationControl theory (sociology)Computer scienceIndex (typography)Electronic engineeringReliability engineeringEngineeringElectrical engineeringVoltageControl (management)Artificial intelligence

Abstract

fetched live from OpenAlex

The Commutation Failure Immunity Index (CFII) is a good predictor which quantifies the susceptibility of an HVDC system to commutation failure. However, this index is based on many electromagnetic simulations and requires extensive computational time. In this paper, a faster calculation method for CFII is proposed and verified. This method significantly reduces the simulation effort and is applicable to single and multi-infeed HVDC system with different short circuit ratios (SCR). The paper gives a comparison of time savings and accuracy of the proposed approach for the study of commutation failure in HVDC systems.

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: none
Teacher disagreement score0.927
Threshold uncertainty score0.747

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 communication0.0000.000
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 head0.042
GPT teacher head0.285
Teacher spread0.243 · 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

Quick stats

Citations7
Published2015
Admission routes1
Has abstractyes

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