Enhancing Electrical Transformer Fault Prediction with Deep Learning: A Focus on ANN-Based Classification
Bibliographic record
Abstract
Electrical transformers play an essential role in the power distribution network, acting as key connectors that facilitate the flow of electricity over large distances to meet diverse power needs. Despite their reliability, the rare occurrence of faults emphasizes the necessity for diligent maintenance to maintain their peak performance. The significant expenses linked to both the maintenance and repair of these components highlight the imperative for innovative maintenance strategies that extend beyond traditional approaches. This situation underscores the urgent need for maintenance strategies that surpass conventional methods. Our study leverages two unique datasets: one featuring data on three-phase currents and voltages and the other including seven essential indicators of transformer health. To predict faults within these datasets, we employ advanced deep learning techniques, specifically using Feedforward Back-propagation neural networks, demonstrating the potential of modern methodologies in enhancing the reliability and efficiency of electrical transformers.
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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".