Design parameter based method of partial discharge detection and location in power transformers
Bibliographic record
Abstract
Insulation defect detection in time ensures higher operational reliability of power system assets. Power transformers are the most critical unit of power systems both from economical and operational front. Hence it becomes necessary to have knowledge of the actual insulation condition of transformer to increase dependability of the system. The performance and ageing of the transformer insulation is mainly affected by Partial discharges (PD). Proper diagnosis in terms of amplitude and location of partial discharge in a power transformer enables us to predict well in advance, with much confidence, the defect in insulation system, which avoids large catastrophic failures. In this work a 20kVA, 230/50kV single phase core type transformer is used for evaluation of the transfer function-based partial discharge detection and location using modeling of the winding, using design data. The simulation of capturing on-line PD pulses across the bushing tap capacitor is done for various tap positions. Standard PD source model is used to inject PD pulse signal at 10 tap locations in the winding and corresponding response signatures are captured at the bushing tap end (across 1000pF). The equivalent high frequency model of the winding is derived from the design parameters using analytical calculations and simulations in packages such as MAGNET and ANSOFT. The test conditions are simulated using ORCAD-9 and the results are evaluated for location accuracy using design parameter based PD monitoring method. .
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".