Partial Discharge Based Risk Assessment Framework for MV Switchgear Containing Electrical Defects
Bibliographic record
Abstract
Partial discharge (PD) diagnostics is regarded as one of the powerful methods for the detection of potential electrical insulation defects in a medium voltage (MV) and high voltage (HV) switchgear. This article proposes a risk-based approach to identify the severity of several critical electrical defects in MV/ HV switchgear through PD testing. To accomplish this, several defects have been artificially created in a MV switchgear. The testing was carried out in the laboratory to investigate the characteristics of PD signals using D-dot sensor previously used and validated by the authors. Accordingly, the specific PD intensity of the discharge pulse has been considered as stress parameter and statistically modeled by an appropriate probability distribution. In this way, the probability of dielectric failure has been statistically quantified. The consequences of dielectric failure, and hence, the failure of the switchgear have been presented in terms of power outage cost and repair expenditures. The risk assessment is carried out by combing the probability of failure and its consequence. The estimated risk is an indication of the severity 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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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 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".