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Record W2767627991 · doi:10.1109/ias.2017.8101885

A new digital protection for grid-connected battery storage systems

2017· article· en· W2767627991 on OpenAlexaff
S. A. Saleh, R. McSheffery, Ryan Meng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicIslanding Detection in Power Systems
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsBattery (electricity)Electrical engineeringRelayModular designGridComputer scienceCircuit breakerFault (geology)Power (physics)Engineering

Abstract

fetched live from OpenAlex

In this paper, a new digital protection is developed and tested for grid-connected battery storage systems (BSSs). The proposed protection is designed using multiple digital relays that are located in different parts of the protected BSS. Each digital relay is featured with a phaselet-based fault detection. The outputs of the developed protection are trip signals to operate circuit breakers in the charging and discharging circuits, battery units, and point-of-common-coupling. The developed modular digital protection is implemented for simulation testing on a 250 kW grid-connected BSS that is charged through a 3φ ac-dc power electronic converter (PEC), and discharged through a 3φ dc-ac PEC. Simulation results show that the developed protection can initiate fast, accurate, and reliable responses to faults occurring in different parts of the protected BSS. These response features have negligible sensitivity to the type and/or location of faults, charge/discharge cycles of the BSS, and/or levels of power exchange with the host power system.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.018
GPT teacher head0.213
Teacher spread0.195 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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