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Record W4416996737 · doi:10.20906/cba2024/4428

Analysis of Distributed Generation’s Impact on the Protection System of Power Distribution Systems

2024· article· W4416996737 on OpenAlexaff
Rodrigo C. D. de Lima, Matheus Dantas de Lucena, Ramayana L. P. Araújo, Núbia Silva Dantas Brito

Bibliographic record

VenueCongresso Brasileiro de Automática · 2024
Typearticle
Language
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsCarbon Engineering (Canada)
Fundersnot available
KeywordsDistributed generationElectric power systemKey (lock)ElectricityElectricity generationDistributed power generationPower (physics)Power-system protection

Abstract

fetched live from OpenAlex

Over the past years, there has been a significant increase in the generation of electricity close final consumers, called distributed generation. Despite the recognized advantages, this sort of generation can cause several problems, particularly to the protection system of traditional distribution systems. Therefore, studying the impacts that distributed generation (DG) can promote on the protection of Electric Power Distribution Systems (EPDS) is a key factor, since the insertion of DG can modify the values of EPDS currents and the protection that was adjusted for a scenario without DG may not act correctly with the presence of photovoltaic generators (PV), due to the absence of monitoring and control of these units. Thus, the results obtained from modeling the IEEE 34-bus system and the devices commonly used in EPDS protection, i.e., fuses, relays and reclosers, indicate that there is a disposition for problems in protection coordination, requiring adjustments in the parameterization stage of protection devices to ensure continuity of service and respect for protection philosophy.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.020
GPT teacher head0.266
Teacher spread0.247 · 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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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