A cascade of artificial neural networks to predict transformers oil parameters
Bibliographic record
Abstract
In this paper artificial neural networks have been constructed to predict different transformers oil parameters. The prediction is performed through modeling the relationship between the insulation resistance measured between distribution transformers high voltage winding, low voltage winding and the ground and the breakdown strength, interfacial tension acidity and the water content of the transformers oil. The process of predicting these oil parameters statuses is carried out using various configurations of neural networks. First, a multilayer feed forward neural network with a back-propagation learning algorithm was implemented. Subsequently, a cascade of these neural networks was deemed to be more promising, and four variations of a three stage cascade were tested. The first configuration takes four inputs and outputs four parameter values, while the other configurations have four neural networks, each with two or three inputs and a single output; the output from some networks are pipelined to some others to produce the final values. Both configurations are evaluated using real-world training and testing data and the accuracy is calculated across a variety of hidden layer and hidden neuron combinations. The results indicate that even with a lack of sufficient data to train the network, accuracy levels of 84% for breakdown voltage, 95% for interfacial tension, 56% for water content, and 75% for oil acidity predictions were obtained by the cascade of neural networks.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".