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Record W1982009019 · doi:10.1109/icps.2014.6839182

Impacts of grounding configurations on responses of ground protective relays for DFIG-based WECSs

2014· article· en· W1982009019 on OpenAlexaff
S. A. Saleh, A. S. Aljankawey, Ryan Meng, Chris Diduch, Liuchen Chang, Julian Meng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHVDC Systems and Fault Protection
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsGroundDoubly fed electric machineWind powerInduction generatorComputer scienceReliability engineeringElectrical engineeringEngineeringVoltageAC power

Abstract

fetched live from OpenAlex

One of the requirements for safe, stable, sustainable, and profitable operation of doubly-fed induction generators (DFIGs)-based wind energy conversion systems (WECSs) is the accurate and reliable protection against electrical faults, in particular ground faults. The performance of protective devices employed to achieve this requirement is highly dependent on the grounding configuration of the DFIG-based WECS. This paper investigates impacts of the grounding configuration on the performance of protective devices used to protect DFIGs-based WECSs from electrical ground faults. Investigated grounding configurations include solid-grounding, low-resistance grounding, high-resistance grounding, and no-grounding. The impacts of the grounding configurations on protective devices are observed through their ability to identify faults, as well as their speed to respond to identified faults. Simulation and experimental results reveal that adequately designed low-resistance grounding offers the minimum impacts on protective devices used for ground protection of DFIG-based WECSs.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
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 head0.020
GPT teacher head0.259
Teacher spread0.239 · 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 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

Citations38
Published2014
Admission routes1
Has abstractyes

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