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Record W2057356736 · doi:10.1109/icpst.2006.321640

Transient Recovery Voltage Assessment for 138kV Breakers with the New Addition of a Wind Farm

2006· article· en· W2057356736 on OpenAlexaff
Zheng Zhou, Xuegong Wang, Paul Wilson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsResearch Manitoba
Fundersnot available
KeywordsCircuit breakerTransient recovery voltageTransient (computer programming)WaveformWind powerElectric power systemElectrical engineeringPower (physics)Power networkVoltageDistribution boardEngineeringDisconnectorAutomotive engineeringComputer sciencePower factorPhysicsConstant power circuit

Abstract

fetched live from OpenAlex

Increased short circuit current level due to rapid growth of power network could reach and possibly exceed the capability of existing breakers. An assessment study of existing breakers needs to be performed to guarantee the breakers can operate safely at present and also in the future. A new proposed wind farm is added to a nearby substation. One concern is how the wind farm impacts the substation power system. This paper presents the assessment of the substation power system and determines if transient recovery voltage (TRV) exceed the breaker capability. The assessment was carried out by digital simulation using PSCAD to analyze TRV waveforms. This paper presents the detailed setting-up of the digital model of the substation and surrounded power system. Based on the output, the peak values and rate of rise of the TRV waveforms can be determined. These values were compared with breaker ratings calculated by IEC standard 62271-100. The studies indicate that breaker TRV does not exceed the capability and the breaker can operate safely with the new addition of a wind farm.

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)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.196
Teacher spread0.191 · 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

Citations6
Published2006
Admission routes1
Has abstractyes

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