Impulse generator optimum setup for transient testing of transformers using frequency-response analysis and genetic algorithm
Bibliographic record
Abstract
Summary form only given. A novel optimization-enabled electromagnetic transient simulation approach is proposed for calculating the resistor setting of a multistage impulse generator for insulation impulse testing of high-voltage transformers. This approach enables test engineers to overcome most of the major challenges attributed to the use of conventional trial-and-error methods for determining resistor settings, including the excessive time consumption and the potential damage to the test transformer due to a greater number of trial tests. The proposed method uses the frequency response of the test transformer to synthesize a circuit model for it. The test setup, including the test transformer and the impulse generator, is simulated using an electromagnetic-transients-type simulator. A genetic-algorithm-based approach is used to optimize the setting of the impulse generator. Optimized resistor values are then used in impulse testing of a three-phase power transformer. Different cases of impulse generator resistor arrangements are studied in this paper and simulated waveforms are compared with those obtained from measurements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".