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Record W3163186756 · doi:10.1109/tia.2021.3079376

Testing a Bus-Differential Protection for Buses Interconnecting Battery Storage Systems

2021· article· en· W3163186756 on OpenAlexafffund
S. A. Saleh, E. Ozkop, Claudio S. Mardegan, M. E. Valdesc

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsUniversity of New Brunswick
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFault (geology)Differential protectionSlack busDifferential (mechanical device)InterconnectionBusbarEngineeringSensitivity (control systems)Battery (electricity)Local busPower (physics)VoltageElectrical engineeringSystem busAutomotive engineeringComputer scienceControl busElectronic engineeringAC powerPower-flow studyComputer hardwareTelecommunications

Abstract

fetched live from OpenAlex

This article presents the implementation and performance testing of a bus-differential protection for buses, which interconnect battery storage systems (BSSs). The proposed bus-differential protection is based on employing the αβ0 components of the apparent powers flowing in all branches of the protected bus. The αβ0 components of the apparent powers are employed due to their ability to accommodate changes in the power flow direction in the branches connected to the protected bus. The apparent powers are determined using the measured voltage and currents in all branches connected to the protected bus. The proposed αβ0-based bus-differential protection is implemented and tested for internal and external faults, which are created during the charging and discharging of BSSs. Test results demonstrate the ability of the proposed αβ0-based bus-differential protection to initiate fast, accurate, and reliable responses to internal and external faults in the protected bus. Observed response features of the proposed protection are found to have minor sensitivity to the mode of operation of the BSS (charging and discharging), fault location (internal and external), and/or fault type.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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: none
Teacher disagreement score0.958
Threshold uncertainty score0.977

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.061
GPT teacher head0.287
Teacher spread0.225 · 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 teacher head, 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

Citations24
Published2021
Admission routes2
Has abstractyes

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