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Record W3163540020 · doi:10.1109/tia.2021.3079878

Single-Phase Ferroresonance in an Ungrounded System During System Energization

2021· article· en· W3163540020 on OpenAlexaff
Iraj Rahimi Pordanjani, Xiaodong Liang, Yunfei Wang, Astried Schneider

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2021
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic Properties and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsFerroresonance in electricity networksOvervoltageTransformerEngineeringSurge arresterElectric power systemRelayProtective relayLightning arresterSurgeElectrical engineeringPower system simulationPower-system protectionControl theory (sociology)Reliability engineeringPower (physics)VoltageComputer sciencePhysicsControl (management)

Abstract

fetched live from OpenAlex

Ferroresonance often occurs in power systems during system switching, which creates overvoltage situations and poses great risks to the equipment safety. In this article, a single-phase ferroresonance incident during a system energization process is presented, which occurred at a single-phase station service transformer in an ungrounded system, causing a surge arrester's failure. This incident is investigated through the PSCAD simulation and the analytical analysis. To validate the PSCAD simulation model, the relay field record is compared with the PSCAD simulation results. Several case studies are conducted using PSCAD simulation to evaluate contributing factors to this ferroresonance incident. The root cause is found to be lack of ground in the system during energization. Field practices are recommended to avoid such incidents, which were proved to be effective by successfully energizing the system in the field.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.029
GPT teacher head0.264
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 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

Citations21
Published2021
Admission routes1
Has abstractyes

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