Challenges and Remedy for Synchronous Generators Asynchronous Operation Following a Fast Loss of Field Detection
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".