Terminal measurements based modeling of single phase transformer for condition monitoring
Bibliographic record
Abstract
The major limitation in the electromagnetic modeling of single phase transformers is the need for the design details and core specifications for accurate representation of its equivalent magnetic circuit. In this paper, a transformer modeling technique which requires only the terminal voltage and current measurements as the necessary inputs is proposed. The methodology is not only simple but also its implementation is non-invasive and can be performed without disconnecting the transformer from the supply. The underlying principle is to develop a magnetizing inductance profile based on these terminal measurements and use it in the circuit-based model of the transformer to simulate its dynamic performance. Effects of core saturation, core losses, magnetic core irregularities and imperfections are accounted for in the model, as these conditions are actually reflected in the terminal measurements themselves. For demonstrating the accuracy of the proposed method, it has been satisfactorily implemented on a 150 VA single-phase transformer. This suggests that the technique can be extended for modeling single and three phase transformers of higher ratings in future. The ultimate goal of such a model is to monitor the health of a transformer online and in real-time by comparing actual magnetizing current with the estimated healthy one.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".