Risk Assessment of Electrical Defects in MV Switchgear Using Partial Discharge Diagnostics
Bibliographic record
Abstract
Partial discharge (PD) diagnostic is regarded as a powerful method for diagnosing the potential electrical insulation defects in a medium voltage (MV) and high voltage (HV) switchgear. This paper proposes a method, termed as a risk-based approach, to identify the severity of several critical electrical defects in MVI HV switchgear based on partial discharge testing. To accomplish this, several defects have been artificially created in a MV switchgear. Testing was carried out to investigate the characteristics of PD signals using nonintrusive sensors. Accordingly, the specific PD intensity of the discharge pulse has been considered as stress parameters and then statistically modelled by suitable probability distribution. In this way, the probability of dielectric failure has been quantified. The consequences of dielectric failure, and hence the failure of the switchgear are given by the power outage cost and repair expenditures. The risk assessment is made by combing both of these factors. The estimated risk is an indication of the criticality of the fault and can be specified as low, medium, high, or maximum. In this way, the proposed approach can be easily adopted by asset manager for risk assessment of switchgear in the petroleum and chemical industries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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 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".