Analysis of HV bushing insulation by dielectric frequency response
Bibliographic record
Abstract
Severe degradation of HV bushings may derive in violent explosions and cause extensive damage to adjacent equipment, power outages, soil contamination, and injuries to personnel working in the proximity. The use of non-destructive and non-intrusive methods to diagnose the overall condition of the insulation system is fundamental. This document describes the experience acquired in the field testing of oil-impregnated paper (OIP) EHV bushings with methods such as capacitance and power factor/dissipation factor (tanδ) measurement and more advanced testing methods, including dielectric frequency response (DFR), and dissolved gas analysis (DGA). DFR, as applied to power transformers, has proven to be a practical tool to determine the moisture concentration in the solid insulation, the contamination in the liquid insulation, and an indicator of the presence of contaminants creating a distortion on the typical dielectric response. Moreover, DFR allows individual temperature correction of power factor/dissipation factor without the use of generic tables. DGA is a widespread method, also applicable for bushings' condition assessment. The DGA interpretation provides end-users fundamental information about an active fault condition caused by an electric or thermal factor affecting the concentration of gases dissolved in the liquid insulation. The information gathered from all these testing methods in the field provided valuable data to better assess the condition of an EHV bushing with respect to its insulation system. The information presented herein will clarify doubts and expand the knowledge regarding the practicality of the above mentioned methods and their potential correlation. Also, the use of the thermal response obtained from the individual dielectric response of each bushing is investigated as a potential mechanism of analysis and diagnostics. The combination of routine and advanced testing techniques provides useful data for asset managers, maintenance and operations staff regarding prioritization of maintenance activities and/or replacement of faulty units.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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) | 0.003 | 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".