A New Loss of Field Detection Scheme for Synchronous Generators by Incorporating a Dynamic Equivalent Circuit Model
Bibliographic record
Abstract
In this paper, a novel loss of field (LOF) detection method is proposed for synchronous generators by utilizing a dynamic equivalent circuit model (DECM). The DECM consists of an internal voltage source and an impedance connected in series, and the two parameters are estimated by measuring three-phase currents, three-phase phase-to-ground voltages, and the rotor speed of the generator. To achieve the proposed detection scheme, an LOF detection index (LFDI) is introduced to detect LOF. To validate the proposed method, an excitation system of the generator using the phase domain (PD) generator model is developed, allowing simulation of various LOF incidents, consistent with IEEE Std. C37.102. The simulated cases include various types of complete and partial LOF events, the stable power swing (SPS) events, and a LOF incidence during the SPS. These cases demonstrate that the proposed LOF detection scheme provides much faster and more secure LOF detection than conventional schemes. The proposed scheme is further validated by experiments in the lab, which demonstrates its feasibility in practical applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".