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Optimal Protection Coordination for Inverter-based Islanded Microgrids Utilizing an Optimized Time-Current-Voltage Characteristic

2023· article· en· W4386952441 on OpenAlexaffabout
Youssef H. El Gohary, Talal Elemamali Sati, Ahmed Osman, Mostafa F. Shaaban

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Windsor
FundersAmerican University
KeywordsBackupMicrogridOvercurrentRelayComputer scienceTrippingInverterVoltageMargin (machine learning)Fault (geology)Linear programmingPower-system protectionProtective relayControl theory (sociology)SoftwareControl engineeringElectric power systemEngineeringPower (physics)Electrical engineeringCircuit breakerAlgorithm

Abstract

fetched live from OpenAlex

With the higher utilization of inverter-interfaced distributed generators (IIDGs) into the distribution networks, major protection challenges became more prevalent, as they are characterized by their low fault current contribution which results in a narrow coordination margin between primary and backup relays. This paper proposes an optimized time-current-voltage tripping characteristic for directional over-current relays (DOCRs). The goal is to minimize the overall operation time of relays as much as possible, and this is achieved by formulating the optimal protection coordination (OPC) problem as a constrained non-linear programming problem, which is then solved using the precise mathematical tools provided by the General Algebraic Modeling System (GAMS) optimization software. Lastly, the suggested relay characteristic is evaluated on a radial microgrid with nine buses, which is a component of the urban Canadian distribution network, to verify its effectiveness.

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 score0.935
Threshold uncertainty score0.975

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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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
Published2023
Admission routes2
Has abstractyes

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