Excitation System Output Quantities-Based Loss of Excitation Detection in Synchronous Generators
Bibliographic record
Abstract
In the earliest generation of loss of excitation (LOE) protection relays, the exciter’s output voltage (Ve) or output current (Ie) were employed for the LOE protection of synchronous generators (SGs) by employing under-voltage or under-current schemes. This paper explores using the excitation system’s output quantities (VeandIe) to detect the LOE phenomenon in SGs. The phase domain (PD) model of SGs available in the real-time digital simulator (RTDS) is used in this paper rather than the well-known dq representation, because only the PD model can provide realistic modeling of the LOE phenomenon based on the IEEE Standard C37-102, andVeandIemeasurements. A new combined scheme usingVeandIeis proposed for the LOE protection as an LOE failure causes an interruption onVeorIe. Through simple paralleled under-voltage and under-current logics, such interruptions can be easily detected. The proposed method is compared with conventional impedance-based schemes through case studies, including the complete LOE (CLOE) and partial LOE (PLOE) failures, and the stable power swing (PS) phenomenon, showing superior performance by reliably detecting CLOE events and maintaining secure operations during PS events, although it may perform unreliably during PLOE events.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".