Evaluation of a calibration technique in measuring partial discharges inside mineral oils with a high‐frequency current transformer (HFCT) sensor: A case study
Bibliographic record
Abstract
Abstract Partial discharge is one of the main reasons for insulation degradation in high‐voltage apparatuses. Partial discharge is an ineluctable phenomenon that happens when a high electrical field is adjacent to an insulator. High‐frequency current transformers are widely used for detecting partial discharge current in high‐voltage equipment. Determination of the apparent charge in pico coulombs is essential for having a common understanding of results and verifying that the measuring system can obtain specified partial discharge magnitude correctly. To this end, a high‐frequency current transformer sensor and a partial discharge calibrator have been prepared to measure the current of the partial discharge (PD) pulses inside mineral oil. The constructed PD calibrator has been evaluated. The high‐frequency current transformer (HFCT) sensor has been simulated using a mathematical model‐based AC analysis and has been characterized and then calibrated using a calibration technique to measure the apparent charge of the PD pulses. Experimental results show the effectiveness of the HFCT sensor and the proposed calibration method.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".