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Simultaneous Application of Communication-Assisted Dual Setting DORs and Hybrid Cuckoo-Linear Algorithm in Optimizing FCL-Oriented Protection Schemes of Microgrids

2020· article· en· W3138135204 on OpenAlexaboutno aff
Amir Mohammad Nakhaee, Ehsan Gadari, S.H.H. Sadeghi

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsnot available
Fundersnot available
KeywordsOvercurrentComputer scienceLinear programmingMicrogridBenchmark (surveying)Cuckoo searchDistributed generationDual (grammatical number)Integer programmingMathematical optimizationInterior point methodOptimization problemCuckooAlgorithmEngineeringRenewable energyVoltageMathematicsElectrical engineering

Abstract

fetched live from OpenAlex

The protection coordination of overcurrent relays in a microgrid with multiple distributed energy resources poses a highly constrained non-linear programming problem. To overcome optimization challenges involved in the treatment of such a problem, heuristic algorithms have been widely welcomed. The purpose of the present paper is to establish the protection coordination between a Fault Current Limiter (FCL) and Dual Setting Directional Overcurrent Relays (DSDORs) in a microgrid, which is equipped with low latency and low bandwidth communications. This is done by a balanced combination of Cuckoo Optimization Algorithm (COA) and Linear Programming (LP). The proposed hybrid COA-LP algorithm is employed to optimize the coordination problem of DSDORs while obtaining the optimal size of the FCL at the Point of Common Coupling (PCC). The effectiveness of the proposed technique is verified via the Canadian distribution benchmark test system, and the results are discussed in depth.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.720
Threshold uncertainty score0.616

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 head0.008
GPT teacher head0.222
Teacher spread0.215 · 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 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
Published2020
Admission routes1
Has abstractyes

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