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Record W2905386572 · doi:10.1109/tia.2018.2886891

Comparing the Performance of Protection Coordination and Digital Modular Protection for Grid-Connected Battery Storage Systems

2018· article· en· W2905386572 on OpenAlexafffund
S. A. Saleh, Christian M. Richard, X. F. St. Onge, Julian Meng, Eduardo Castillo-Guerra

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2018
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsModular designInterlockingGridBattery storageComputer scienceEmbedded systemEngineeringReliability engineeringBattery (electricity)Operating system

Abstract

fetched live from OpenAlex

This paper compares the performances of protection coordination [time setting and zone selection interlocking (ZSI)] and digital modular protection, when deployed for grid-connected battery storage systems (BSSs). The comparison between these protection management methods is made in terms of their structures, functionalities, and response capabilities. These criteria are selected to demonstrate possible impacts of the system configuration and mode of operating a grid-connected BSS on protection responses. The performance comparison among the time-setting coordination, the ZSI coordination, and the digital modular protection is conducted for different grid-connected BSSs, when operated for various fault and non-fault conditions. Performance results show that the protection coordination can offer a simple structure that is set to achieve specific response (TRIP and RESTRAIN). In addition, performance results show that the digital modular protection can offer diverse responses (TRIP, RESTRAIN, and ACTIVATE), which mandate for a digital implementation.

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)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.221
Teacher spread0.198 · 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 designBench or experimental
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

Citations23
Published2018
Admission routes2
Has abstractyes

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