Corrosive Dibenzyl Disulfide Concentration Prediction in Transformer Oil Using Deep Neural Network
Why this work is in the frame
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Bibliographic record
Abstract
Dibenzyl disulfide (DBDS) is the most prevalent corrosive sulfur in transformer oil. It reacts with the transformer windings to produce copper sulfide (Cu2S) and gets deposited on the insulating paper’s surface, leading to interturn faults within the transformer windings. Hence, this article proposes a deep neural network (DNN) to predict the DBDS content in transformer oil. The parameters like interfacial tension (IFT), breakdown voltage (BDV), water content (WC), oxygen, neutralization number (NN), color, furan content, and specific gravity (SG) were used as features to train and test the DNN model. The performance of the regression model was evaluated using mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R}^{{2}}{)}$ </tex-math></inline-formula> . Moreover, extensive analysis is carried out by varying feature combinations and test-train ratios to obtain the best prediction model. The values of DBDS predicted by DNN were further used to determine the corrosive sulfur concentration in transformer oil. The proposed method is validated on real-life transformer data obtained from the online dataset and on data obtained from the local power utilities. A comparative study showed better efficacy of the proposed DNN model than other prediction models for accurate DBDS prediction.
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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 | 0.002 |
| 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) | 0.000 | 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 it