Non-Invasive Detection of Rotor Inter-Turn Short Circuit in Large Hydrogenerators by Using Stray Flux Measurement Combined With Convolutional Variational Autoencoder Analysis (CVAE)
Bibliographic record
Abstract
Salient Pole Synchronous Generators (SPSG) are known for their robustness and stability; However, internal faults like rotor interturn short circuits (ITSC) might still occur and lead to unscheduled machine shutdowns if not caught early. The literature focuses mainly on high-speed SPSGs and is short on studies covering the diagnosis of low-speed machines. To bridge this gap, this paper presents a non-invasive diagnosis method for low-speed SPSG used by Hydro-Québec. The proposed approach is based on real measurements of stray flux signals and faulty synthetic signals, obtained by FEM simulations. The Convolutional Variational AutoEncoder (CVAE) is used to cluster signals according to the fault severity, and to visualize them in 2D space. Furthermore, two studies were conducted to compare the performance and robustness of the CVAE against the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">${\bm{RMS}}$</tex-math></inline-formula> standard method. The results demonstrate that the CVAE is more sensitive and reliable in detecting ITSCs in large hydrogenerators. Finally, a case study was conducted to validate the proposed method using a real faulty dataset, confirming the obtained results.
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".