Condition Evaluation and Fault Diagnosis of Power Transformer Based on GAN-CNN
Bibliographic record
Abstract
Power transformer is one of the most important components of power system. Maintaining its stable operation is an important guarantee for the normal operation of the power system. In recent years, prognostics and health management (PHM) has been introduced into the health management of power transformers. The key information about its operation is obtained by sensors, which provides a platform for intelligent management. At present, for the fault diagnosis and condition assessment of power transformers, due to the lack of original data feature parameters, the lack of data, and the uneven classification of existing data fault types, it is easy to distort the training model. To overcome the above difficulties, this paper proposes a power transformer condition assessment and fault diagnosis method based on generative adversarial network (GAN) and convolutional neural network (CNN). Through GAN, the original data feature parameters are amplified and generate the artificial data set. The data is trained together through CNN. Finally, the validity and superiority of the proposed method are verified by the measured data and the comparative experiment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".