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Record W4407782665 · doi:10.1109/access.2025.3544109

A New Model of Trapped Charge Sources in Switching Transient Studies in the Presence of Shunt Reactors

2025· article· en· W4407782665 on OpenAlexfundno aff
Mohsen Akafi-Mobarakeh, Reza Shariatinasab, Pierluigi Siano, Behrooz Vahidi, Hamid Reza Najafi

Bibliographic record

VenueIEEE Access · 2025
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsnot available
FundersOntario Ministry of Research and Innovation
KeywordsTransient (computer programming)Shunt (medical)Transient analysisComputer scienceNuclear engineeringMaterials scienceTransient responseElectrical engineeringEngineeringMedicineCardiology

Abstract

fetched live from OpenAlex

Insulation coordination studies are of great importance in power grid reliability. In this paper, a new method is proposed for modeling trapped charge sources (TCS) in switching transient studies. The TCS is used to take into account the voltage stored in line capacitors during reclosing operation after fault occurrence. The proposed model is designed based on the active filter concept, thus overcoming the limitations of conventional TCS for simulating transient states in EMTP/ATPDraw. Given the natural frequencies of a transmission line to which the proposed TCS (PTCS) is connected, it injects the appropriate frequencies and eliminates voltage oscillations which limit the use of TCS. To verify the efficiency of the PTCS, it is implemented in a real system with a shunt reactor, and the results are then compared with field measurements. A comparison of the results shows that the PTCS eliminates the voltage oscillations in the simulation before closing and provides a smooth voltage with the desired amplitude. Using the proposed model, the maximum line switching overvoltage is correctly calculated; this, in turn, results in a more accurate transmission line insulation design, which is technically and economically beneficial.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.359

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 science0.0010.000
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 head0.071
GPT teacher head0.342
Teacher spread0.271 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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