Field Winding Short Circuit Fault Signature Analysis in Stray Flux of large Hydrogenerator
Bibliographic record
Abstract
The hydroelectric power plant is expected to provide a consistent source of electricity to the grid. If a fault does occur, the maintenance team is usually quick to arrange corrective maintenance to repair the equipment, as a generator shutdown can lead to huge financial losses. This prompt response limits the amount of faulty data. Furthermore, the equipment is made for industrial use and cannot be used for testing, so it is not possible to implement faults to create faulty bench tests. As a result, there is a lack of faulty signals for large hydrogenerators that are necessary to train artificial intelligence algorithms to diagnose faults. This work presents a method to augment and complete a balanced faulty database. The proposed method consists of generating faulty synthetic signals based on in-situ stray flux measurements and fault signatures deduced from simulated signals. To compute external magnetic flux in healthy and faulty cases, a 2D finite element model of a 370M VA salient-pole synchronous generator was created, validated, and used to extract field winding short circuit fault signatures of several severities.
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 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 | 0.001 | 0.002 |
| 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) | 0.001 | 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 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".