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Record W4417439148 · doi:10.1109/tia.2025.3645026

Challenges and Remedy for Synchronous Generators Asynchronous Operation Following a Fast Loss of Field Detection

2025· article· W4417439148 on OpenAlexaff
Abbas Hasani, Xiaodong Liang, Majid Sanaye‐Pasand, Moein Abedini

Bibliographic record

VenueIEEE Transactions on Industry Applications · 2025
Typearticle
Language
FieldEngineering
TopicPower Systems Fault Detection
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsSynchronismTrippingAsynchronous communicationSynchronizerStatorOverheating (electricity)Generator (circuit theory)Control theory (sociology)Electric power system

Abstract

fetched live from OpenAlex

Asynchronous operation of a synchronous generator (SG) following a loss of field (LOF) incident can lead to potential mechanical damages, as well as overheating in the stator winding, rotor body and stator end core region. To protect SGs from being damaged, power plant protection codes recommend prompt LOF detection and tripping of SGs. This study quantitatively evaluates how the severity of LOF consequences depends on the SG's initial loading. Unlike the conventional LOF (ANSI 40) and loss of synchronism (ANSI 78) protections that immediately disconnect the SG, this paper introduces a remedial control action (RCA) that departs from the traditional “detect and-trip” approach. The RCA reduces the generator output after a fast LOF detection to achieve two objectives: 1) to prevent the SG from entering a hazardous state, and 2) to allow time for either restoring the excitation system or executing a controlled shutdown of the SG. LOF detection can be performed using a fast and readily available scheme that triggers the RA. The simulated case studies demonstrate that the proposed RA enables safe asynchronous operations of the SG after an LOF incident, and also provides an opportunity to either restore the SG's excitation system or initiate a planned outage.

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), Research integrity
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 score0.967
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.015
GPT teacher head0.266
Teacher spread0.251 · 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 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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