MétaCan
Menu
Back to cohort
Record W1541387583 · doi:10.1109/tdc.2006.1668603

Practical Solutions to Protecting Extra Large Split-Phase Autotransformers

2006· article· en· W1541387583 on OpenAlexaff
Lubomir Sevov, B. Kasztenny, Jeong-Yeol Han

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsGeneral Electric (Canada)
Fundersnot available
KeywordsAutotransformerPower-system protectionTransformerElectric power systemProtective relayIEC 61850InterconnectionCover (algebra)Computer scienceMicroprocessorReliability engineeringEngineeringElectrical engineeringPower (physics)TelecommunicationsDistribution transformer

Abstract

fetched live from OpenAlex

This paper presents protection schemes for an extra large 2,000 MVA 765/345 kV split-phase autotransformer. Equipment of this size is always an integral part of a power system backbone of an interconnection, a country or a continent; and as such it should be protected applying the highest possible standards. Dedicated protection functions have been engineered and configured to cover winding, ground, bank, unit and tertiary faults. The protection system is connected to more than 50 ac signals, and is setup using numerous multi-function transformer and general-purpose microprocessor-based relays. The applied protection schemes incorporate extended supervision in order to avoid false operations that would impact power system operation and stability. The paper describes each protection function in detail including theory of operation, zones of protection, diagrams, setting recommendations, and results of simulation testing. After being fine-tuned and tested on the real time digital simulator (RTDS), the unique protection system presented in this paper is in-service for approximately one year

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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.023
GPT teacher head0.300
Teacher spread0.277 · 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
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

Citations0
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicPower Systems Fault DetectionFrench-language works237,207