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Record W4386372591 · doi:10.3390/en16176348

A Comprehensive Review on Transient Recovery Voltage in Power Systems: Models, Standardizations and Analysis

2023· review· en· W4386372591 on OpenAlexaff
Eleonora Fripp Lazzari, Adriano Peres de Morais, Maicon J. S. Ramos, Renato G. Ferraz, Tiago B. Marchesan, Vitor C. Bender, Rodinei Carraro, Herber Fontoura, Cristian Correa, Mariana Resener

Bibliographic record

VenueEnergies · 2023
Typereview
Languageen
FieldPhysics and Astronomy
TopicLightning and Electromagnetic Phenomena
Canadian institutionsSimon Fraser UniversitySiemens (Canada)
FundersUniversidade Federal de Santa MariaAgência Nacional de Energia ElétricaUniversidade Federal do Rio Grande do SulConselho Nacional de Desenvolvimento Científico e TecnológicoCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsTransient (computer programming)Circuit breakerInterruptElectric power systemTransient recovery voltageVoltageOvervoltageSIGNAL (programming language)EngineeringElectrical engineeringFault (geology)Computer scienceTransient voltage suppressorElectronic engineeringPower (physics)Reliability engineeringSwitched-mode power supplyPhysicsTransmission (telecommunications)Constant power circuit

Abstract

fetched live from OpenAlex

Electrical power systems are exposed to transient disturbances that change the voltage and current signals of the network, which can interrupt power and damage equipment. In high-frequency phenomena, it is essential to study the transient recovery voltage (TRV) to ensure the electrical insulation limits of circuit breakers are not violated, thus leading to a safe and reliable operation. Adequate models are crucial to achieving satisfactory results in the studies, according to the range of frequency of the transient being evaluated. This paper presents a comprehensive literature review of methods and models for studying electromagnetic transients, focusing on TRV requests imposed on circuit breakers, in addition to fault-clearing simulations on real system modeling. The analyses are fundamental both for the evaluation of the amplitude of the voltage signal and for its rate of rise. We also compare the reviewed models and techniques to provide a handy resource for researchers.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.944
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.036
GPT teacher head0.295
Teacher spread0.258 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2023
Admission routes1
Has abstractyes

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