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Performance Assessment of the αβ0-Based Bus Differential Protection

2022· article· en· W4280502524 on OpenAlexaff
S. A. Saleh, E. Ozkop, R. Ahshan, A. Al-Durra, Marcelo E. Valdes, Claudio S. Mardegan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrogrid Control and Optimization
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsFault (geology)Sensitivity (control systems)InterconnectionDifferential (mechanical device)Power (physics)Computer scienceVoltageDifferential protectionBusbarBattery (electricity)Electronic engineeringAutomotive engineeringElectrical engineeringEngineeringTelecommunications

Abstract

fetched live from OpenAlex

This paper presents the experimental performance assessment of the αβ0-based bus differential protection (BDP). This protection is developed for buses that interconnect battery storage systems (BSSs). The tested BDP employs αβ0 components of the apparent powers flowing in all branches that are connected to the protected bus. The αβ0 components of apparent powers allow accommodating frequent changes in the direction of power flows due to the charging/discharging modes of the BSS operation. In each branch connected to the protected bus, the 3φ apparent powers are determined using the measured branch currents and voltage (at the protected bus). The αβ0-based BDP is implemented using a digital processing board for experimental testing for a laboratory setup. The test setup is composed of a bus that interconnects a 4 kW BSS, and feeds 3φ linear and dynamic loads. The performance of the αβ0-based BDP is assessed for responding to various internal and external faults occurring during the charging and discharging of the BSS. Experimental results reveal encouraging abilities of the tested BDP to initiate fast, accurate, and reliable responses to internal and external faults. Observed response features are complimented with a minor sensitivity to the BSS mode of operation, fault type, and/or fault location.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score0.535

Codex and Gemma teacher scores by category

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

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 head0.005
GPT teacher head0.169
Teacher spread0.164 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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